Deliverable 6.3: Emulation of the aggregator management and its interaction with the TSO-DSO

Transcription

1 Project no: Project acronym: IDE4L Project title: IDEAL GRID FOR ALL Deliverable 6.3: Emulation of the aggregator management and its interaction with the TSO-DSO Due date of deliverable: Actual submission date: Start date of project: Duration: 36 months Lead beneficial name: Tampere University of Technology, Finland Writers/authors: RWTH, IREC, KTH, DTU Dissemination level: Public

2 IDE4L Deliverable 6.3 Track Changes Version Date Description Revised Approved /07/2016 First complete version of the deliverable /07/2016 Comments and feedback collected and addressed /07/2016 Final revision and document finalisation Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero, Gerard del Rosario (IREC), Hossein Hooshyar (KTH) Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero (IREC) 2

3 IDE4L Deliverable 6.3 TABLE OF CONTENTS: List of Abbreviations... 5 EXECUTIVE SUMMARY INTRODUCTION Task Contribution Overview of Concepts The IDE4L Architecture in the tests AGGREGATOR EMULATION IN REAL-TIME Assumptions for the emulation Emulation goals Simulation Goal Simulation Goal KPIs and Metrics Setup description Real-Time Commercial Aggregator Algorithm DN and PCC simulators Link of the setup with the KPIs and metrics IREC setup RWTH setup Test Scenarios Winter scenario tests Summer scenario tests Mid-Season scenario tests Simulation results Global interpretation of simulation results DYNAMIC TARIFF (DT) Simulation goals Simulation goal Simulation goal Setup description Test scenarios Test Case

4 IDE4L Deliverable Test case Test case Simulation results Test case Test case Test case Global interpretation of simulation results SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION Emulation goals Setup description IEC gateway for transmission of PMU data (test scenarios and simulation results) IEEE/IEC gateway traffic generation Steady state model synthesis of active distribution networks (test scenarios and simulation results) Test 1: Reproduction of the equivalent model parameters in real-time Test 2: Incorporating the effect of mutual inductances Test 3: Model synthesis of a sample active distribution network Test 4: Sensitivity analysis Voltage stability indicators (test scenarios and simulation results) Test 1: No DG connected to the distribution network Test 2: DG connected to the MV section of the distribution network Test 3: Loss of generation in the transmission network Small-signal dynamic model synthesis and stability indicators (test scenarios and simulation results) Test 1: Mode estimation using centralized architecture Test 2: Mode estimation using decentralized architecture Calculation of KPIs KPI 1 (KPI ID: I) KPI 2(KPI ID: UR) Global interpretation of simulation results CONCLUSIONS Aggregator emulation in real-time Dynamic tariff Synchrophasor applications for enhancing TSO-DSO interaction REFERENCES

5 IDE4L Deliverable 6.3 List of Abbreviations CA CC CRP DER DESS DG DMS DNO DR DSM DSO EEGI EMS ESS EV LA LV MA MLA MV PCC RES RTV TC TSO Commercial Aggregator Control Centre Conditional Re-Profiling Product Distributed Energy Resources Distributed Energy Storage Systems Distributed Generation Distribution Management System Distributed Network Operator (passive) Demand Response Demand Side Management Distribution System Operator (active) European Electric Grids Initiative Energy Management System Energy Storage Systems Electric Vehicles Load Area Low Voltage Market Agent Macro Load Area Medium Voltage Point of Common Coupling Renewable Energy Sources Real Time Validation Tertiary Controller Transmission System Operator 5

6 IDE4L Deliverable 6.3 EXECUTIVE SUMMARY The work presented in this deliverable is about the analysis of the feasibility and the technical viability of the aggregator and its interaction with TSO-DSO and the markets by means of ancillary services. This technical viability is analyzed by implementing and emulating the optimal management and technical control of distribution networks involving the participation of DERs (including microgrids), aggregators and TSO/DSOs. Three different aspects of such management and control have been studied in real-time horizons: Development, implementation and testing of algorithms run by aggregators to activate flexibility volumes of DERs participating in IDE4L congestion management schemes. Tests include the experimental emulation of individual DERs and their interactions with aggregators. Implementation and demonstration of dynamic tariff (DT) algorithms previously designed for efficient congestion management in active distribution networks. In order to demonstrate such algorithms in near real-life environments, tests make use of real-time digital simulators. Demonstration of the performance of selected PMU applications already designed. The analysis of results focuses on indicators on the stability of the power system and TSO s visibility of the distribution network operated by the DSO. The main findings of this work are summarized in the following paragraphs. As a general conclusion, the feasibility and technical viability of the management and control techniques emulated and simulated during this work have been demonstrated. DERs flexibility activation by aggregators Simulation models of UNARETI MV and LV networks as well as realistic customers data have been used to build up worst-case scenarios to test the algorithms. In particular, DERs connected at LV level have been adapted to the different aggregator s clusters. At individual customer level, flexibility sources are (i) commercially-available home energy storage systems (ESSs) and (ii) thermostat regulation of domestic heating and air conditioning (HVAC) systems. Also, existing residential photovoltaics (PV) units have been included in the simulations, but no flexibility is assumed for them in the study. Regarding flexibility available per type of device, ESSs seem to have higher potential to curtail power injected/substracted to/from the grid compared to HVAC. It has been demonstrated that ESSs are a good means to accommodate additional PV power into the system and avoiding related congestion at the same time. When it comes to compliance of flexibility product specifications by aggregators, the algorithm to activate DERs flexibility volumes takes into account possible under/over-performances during the delivery to the flexibility buyer (DSO), for instance as a consequence of unexpected output deviations from forecasted values. It has been demonstrated how pass-through methodologies can be improved including real-time measurements from smart meters as feedback for the algorithm. From DSO s standpoint, congestions consisting of MV branch thermal overloadings have been eliminated via flexibility activation. In these tests, positive side-effects of power curtailment are also voltage level improvements in locations close to the nodes where power is curtailed. Furthermore, active power losses at LV level have an impact 6

7 IDE4L Deliverable 6.3 on the effectiveness of the equivalent power modulated at the MV node of the MV/LV substation where DERs are connected. Dynamic tariff Two optimal management strategies are compared: centralized at the DSO side and distributed at individual aggregators sides. In particular, consumption/load profiles resulting from both strategies are compared in order to check whether rates of the DT produce the expected outcome in near real-life environments or not. Moreover, the effectiveness of the DT to contribute to congestions alleviation is also reported. To this aim, the reduction of peak load in two cases with and without DT- is compared. Three test cases were designed: DERs penetration of 25%, 50% and 75%, respectively. Each test case has two scenarios: the DER planning with the DT method and the DER planning without the DT method. Results reveal that the DT method has a very good convergence as a distributed control method in general. Additionally, the efficacy of the DT method for congestion management is demonstrated since peak loadings are largely reduced by using this method. However, the method used to determine DT neglects (i) power losses, (ii) reactive power consumption and (iii) voltage drops in the network. For MV networks, neglected power losses do not produce significant error compared to more detailed methods, whereas reactive power consumption and voltage drops may produce non-satisfactory results in some specific cases. In those cases, solutions are proposed to overcome this issue. PMU applications Enhancement of TSO s awareness and visibility of downstream distribution systems through PMU applications is demonstrated in this work. In particular, voltage instabilities occurring at distribution level and different grid model synthesis applications are studied. Simulation results and computed indicators show that the exchange of key information obtained from PMU measurements between TSOs and DSOs helps tighter the integration of the operation of HV, MV and LV grids. However, it is highlighted that the incorporation of PMUs in modern IEC61850-based substation automation systems is more complex than desired since a different communication protocol (IEEE standard) is being deployed for PMUs in the majority of EU countries. 7

8 IDE4L Deliverable INTRODUCTION IDE4L Work Package 6 deals with two highly interconnected objectives: the optimal management and the technical control of resources. On its time, these objectives involve three actors: microgrids, aggregators and TSO/DSO. The work presented in this deliverable is about the analysis of the feasibility and the technical viability of the aggregator and its interaction with TSO-DSO and the markets by means of ancillary services. This technical viability is analyzed by implementing and emulating the optimal management and technical control of distribution networks involving the participation of DERs (including microgrids), aggregators and TSO/DSOs. Thus, the main objectives of the work reported in this deliverable are: To define reference distribution and microgrid networks for the study of distribution network dynamics including intermittent sources. To optimize the integration of DER corresponding to dynamic pricing, load balancing and aggregators. To maximize power quality mitigation and equipment degradation resulting from integration. To define tools for microgrids participation in ancillary services provision for active/reactive power. To develop methods for interfacing TSOs and DSOs via key dynamic information exchange. The results and analysis of the aggregator system emulation are reported. 1.1 Task Contribution Deliverables D6.1 and D6.2, results of Task 6.1, 6.2 and 6.3, defined and presented the technical and economic management algorithms [1] [2]. Task 6.4 has been devoted to the integration of these algorithms into a real microgrid and an emulator of a MV grid in order to check the feasibility and the technical viability of such proposals. Deliverable D6.3 collects and analyse the main results of this task. In particular, the work on the aggregator emulation in real time, presented in Section 2, proposes instances of implementation for the participation of aggregators to ancillary services. To this aim, the DSO interacts with the aggregators in order to solve congestions. The consequent improvement in the management of the grid is compared to that of conventional methods. Additionally, microgrids are included -among other conventional demand side flexibility resources- as local entities enhancing the flexibility capacity of the other DERs. Furthermore, relevant DSO levels of interaction are investigated in scenarios with participation of aggregators and high degree of penetration of renewable sources. In this sense, the robustness of DSO control methods as well as their interference with other interventions (e.g. the actions of the aggregator) are tested. Section 3 of this deliverable is devoted to test the dynamic tariff method, as a price-based DR program, for congestion management in a near real-life environment. Section 4 of this deliverable demonstrates the performance and the added value of selected PMU applications, which were introduced in Deliverable 6.2 [11], to carry out an analysis of the interaction between aggregator and TSO-DSO and the emulation of the participation of the aggregator in the ancillary services. In addition, performance of the library, introduced in Deliverable 6.2, to generate and parse the 8

9 IDE4L Deliverable 6.3 Routed-Sampled Value and Routed-GOOSE messages containing Synchrophasor data is demonstrated in this section. The performance of the applications are assessed through computation of KPIs and simulation of different scenarios on the reference active distribution network model that has been developed and introduced in deliverable 6.1, part I [12]. 1.2 Overview of Concepts One of the goals of this work is to test the CA s real-time performance in the CRP Activation [8] Use Case. Here we introduce three main concepts in order to contextualize the Use Case: Demand side flexibility Flexibility provided by network users such as demand response solutions are considered crucial for matching supply and demand in the future in order to be able to evolve to energy-decarbonised scenarios [3][4]. On an individual level, flexibility is defined according to [5] as the modification of generation injection and/or consumption patterns in reaction to an external signal (price signal or activation) in order to provide a service within the energy system. Both supply and demand can provide flexibility, thus demand side flexibility is the term used to refer to industrial, commercial or residential consumers. In this work, demand side flexibility is activated as an ancillary service in close-to real-time schemes, in contrast to D6.1 where flexibility was traded in day-ahead time scales. Aggregation of demand side flexibility from small resources In particular, flexibility provided by relatively small generating and load resources -residential, commercial and alike- must be traded in electricity markets or other trade platforms -bilateral contracts, call for tenders- by means of the aggregation of a number of such resources given that they do not have neither the means nor the size to trade directly in those platforms [6] Aggregators are commercial entities -either a third party aggregator or the customer s supplier - in charge of pooling decentralised generation and/or consumption to provide energy and services to actors within the system. Conditional Re-Profiling product A specific flexibility product is studied in this deliverable in which the capacity for a specified generation/demand modification is previously procured for a specific period of time but the activation is optionally triggered by a control signal from the flexibility buyer at short notice. This product is based on the conditional re-profiling (CRP) product described for active demand in the ADDRESS project [7]. In this type of flexibility product, sellers and buyers trade controllable power, i.e. deviation from the forecasted level of demand called baseline-, and not a specific level of demand. This work also relies on results from D6.1 Part II and D within the context of the IDE4L project. In the following two sections the interactions with such deliverables are briefly reported. 9

10 IDE4L Deliverable The IDE4L Architecture in the tests Regarding IDE4L s architecture, the following assumptions apply for the tests: Commercial Aggregator The aggregator schema used in this work is based upon the Commercial Aggregator (CA) concept and architecture developed in Deliverable 6.1. In this previous Deliverable it was defined the different functions included in the architecture of the CA that have to be implemented in order to build its participation in the market and to organize the interaction with the DSO-TSO. In particular, the existence of several of the CA architecture functions is assumed in this work, including: Consumption forecasting: Consumption forecast is used for estimating the consumption baseline that is used afterwards for flexibility services quantification. A baseline is important to calculate the effective service provided by the aggregation service provider. Consumer Segmentation (Clustering): Every cluster comprises a group of prosumers sharing some key characteristics for flexibility provision such as a similar consumption pattern, kind of contract, kind of appliances included, or existence (or not) of Energy Storage Systems (ESS). Flexibility Forecast Tool: This tool simulates the behaviour of consumers under different volume signals. Consumers/prosumers This work also assumes the existence of Energy Management Systems (EMS) in consumers/prosumers premises. Such devices coordinate load, generation and storage at consumer premises as well as reschedule the consumption profile of the consumer/prosumer when remote signals are received from CAs for flexibility activation purposes. To summarize, at least a device acting as a gateway between prosumers and CA is needed for each of the network users included in the consumers/prosumers portfolio. DSO The Distribution System Operator (DSO) is the flexibility buyer assumed in this work. From the DSO perspective, flexibility connected to its distribution network should be integrated as part of its control systems. In particular, the DSO is assumed to have the role of flexibility procurer to feed its Distribution Management System (DMS) and its functions to hinder network congestion. In real-time framework, the DSO uses a tool for deciding on the activation of already procured CRPs called Real-Time Validation (RTV). The RTV tool is used for different purposes, including solving potential grid constraints arising in the short term by means of activating/curtailing already procured CRPs. This function runs on demand, every time there is a request from DSOs centralized control systems and a few minutes before flexibility is actually delivered from CAs. Additionally, from a DSO s point of view, consumers/prosumers are grouped per Load Areas (LAs) and Macro Load Areas (MLAs) in a way that they provide the observability and the information required for all the functions of the DSO as well as the TSO. Such network users groups are defined according to their load/generation patterns, impedances and alike so that all users belonging to the same LA virtually have the same impact on the network operating constraints. Every consumer can be identified within any LA or MLA by means of the Macro Load Area code, the Load Area code and the consumers ID. Hence, DSOs and 10

11 IDE4L Deliverable 6.3 TSOs can place consumers geographically in the network when dealing with RTV and flexibility products acquisition/activation. Figure Detailed interactions of congestion management (IDE4L deliverable D ). Tests implemented in T6.4 are highlighted in a green box. From the DSO s architecture viewpoint, CRP products activation is triggered within the framework of their Tertiary Controller (TC), Figure 1.1. The TC takes responsibility to purchase CRP services from CAs if needed to solve congestions. In particular, the DSO s Control Center (CC) is assumed to be able to manage MV networks by means of market-related measures to propose changes of scheduled generation/consumption values of DER units, through flexibility offers/bids to provide a feasible combination of schedules. This function is called Market Agent (MA) in D It is worth highlighting that flexibility products assume the selling of additional energy (generation increase or load decrease) but not the buying of additional energy (generation decrease or load decrease). Scenarios where congestion is caused by high local energy production from renewable energy sources (RES) and low local demand thus potentially needing the buying of additional energy in order to maximize RES production- are out of the scope of the MA tool. Alternatively, flexibility volumes activated in such type of scenario are not generated through the MA tool but given as non-optimized inputs. 11

12 IDE4L Deliverable AGGREGATOR EMULATION IN REAL-TIME 2.1 Assumptions for the emulation This work is devoted to the implementation of algorithms to emulate CAs delivering flexibility to DSOs operating congested grids in real-time scales. Flexibility offered by CAs (CRP product) is here used as a means to contribute to congestion relief. Some considerations and previous hypothesis are provided regarding these emulations: Flexibility is an active power modulation and this is an effective method to reduce overload in lines/transformers as well as voltage level violations especially at distribution grid level-. DSO s MA tool optimizes congestion management at MV level. LAs are assumed to be defined at the MV side of MV/LV substations serving residential network users. Customers/prosumers in CAs portfolios are residential network users. When certain LA becomes congested, the DSO is aware of the aggregated total amount of flexibility offered by the different CAs operating consumers/prosumers in that congested LA, but it has no knowledge on how this flexibility is allocated among the customers. CAs do not have grid locational information of their customers/prosumers portfolio and thus they are not able to optimize active power losses in the LV network of the congested LA. The individual flexibility allocation strategy followed by CAs is similar to the curtailment of flexibility products executed by the RTV tool described in D6.1: individual curtailment volumes proportional to individual available flexibilities are allocated per eligible consumer/prosumer connected to the congested LA. The summation of all individual allocated volumes equals the CRP volume activated by the DSO. 2.2 Emulation goals The following table summarizes the emulation goals of the Commercial Aggregator: Table 2-1 Emulation goals of the Commercial Aggregator WP6 and T6.4 goals Emulation goals Metrics groups Related KPIs Importance Check the feasibility and the technical viability of real-time function of Commercial Aggregator Architecture Improve power quality issues resulting from integration of DERs/microgrids verify if the real-time CA manages to fulfil the flexibility requirements in closeto-real conditions verify that the DSO enhances the management of the grid with the contribution of aggregated flexibility DERs/microgrids Related to CA Related to DSO Related to Consumers/Prosumers B.5 Increased flexibility from energy players (DSO) B.3 Power Quality and Quality of Supply (line voltage profiles according to EN 50160) B.2 Reduced energy curtailment of RES and DER How does the proposed IDE4L CA architecture perform in realtime scales? Does real-time CA successfully integrate in IDE4L congestion management schemes? 12

13 IDE4L Deliverable 6.3 It should be highlighted that two different perspectives are considered: from the CA s point of view, the real-time performance of its architecture is addressed in this work, and from the DSO s point of view, the interaction of the CA with the Market Agent in the IDE4L congestion management scheme is also studied Simulation Goal 1 How does real-time CA manage to fulfill requirements for flexibility performance in close-to-real conditions? (CA viewpoint) Traditionally, demand response programs including the aggregation of diverse customers have already been implemented in a number of countries with performance levels fulfilling market requirements for reliability [9] However, individual customers -especially residential ones- typically do not have the same technical capabilities than other types of network users such as industrial loads or generation units and thus their deliveries are more prone to deviate from activated flexibility volumes. It is common practice that aggregators do not bid their full available volume to the market in order to take into account the nonperforming individual customers. Another frequent practice is that programs allow for over-delivery of flexibility but this of course results in inefficiencies of this service. In this work, an algorithm to allocate the flexibility volume among the available flexibility providers connected to the same LA is developed. The algorithm is intended to be used by one aggregator in such LA and corrects possible failures to deliver by checking recent energy measurements from its flexibility providers. The aim of this the proposed implementation is to help increase the efficiency of flexibility delivery from residential network users to the power system and contribute to a better utilization of their resources Simulation Goal 2 How can DSO enhance management of the grid with the contribution of flexibility from aggregated DERs/microgrids? (DSO viewpoint) Flexibility provided by relatively small generating and load resources -residential, commercial and alikemust be traded in electricity markets or other trade platforms -bilateral contracts, call for tenders- by means of the aggregation of a number of such resources given that they do not have neither the means nor the size to trade directly in those platforms. Currently, the demand response solutions implemented in a significant number of countries do not consider aggregation of customers/prosumers at LV level but typically focus on fewer resources of greater individual size (i.e. industrial loads) connected to MV and HV levels. However, an increasing penetration of DERs (energy storage systems (ESSs), electric vehicles (EVs), small RES, microgrids and alike) in LV grids will add additional flexibility potential to the power system but also will likely push that system to congestion conditions more easily KPIs and Metrics The following table shows the KPIs and the metrics used in the emulation tests to assess the goals. A brief description of each KPI and metric follows. 13

14 IDE4L Deliverable 6.3 Table 2-2 List of KPIs and metrics used in the emulation tests KPIs B.5 Increased flexibility from energy players (DSO) B.3 Power Quality and Quality of Supply (line voltage profiles according to EN 50160) B.2 Reduced energy curtailment of RES and DER Metrics (related to CA) Demand capability response Line voltage profiles fulfilling grid nominal voltage requirements Real time performance of CA (=flexibility delivery from CA to DSO) Flexibility activated per cluster Metrics (related to DSO) Metrics (related to Consumers/Prosumers) Congested branch loadings Power losses in LA Active power measurements (PCC, ESS, RES, residential load) B.5 Increased flexibility from energy players (DSO). B.5.1 Demand response capability From the power system point of view, one of the key contributions of customers/prosumers aggregation is the enhancement of the system s flexibility with the participation of relatively small network users in flexibility-related ancillary services. The KPI named B.5 Increased flexibility from energy players (DSO): B.5.1 Demand response capability is used in this case to compare the cases with and without the existence of aggregated flexibility provided by the CA (winter scenario). Such KPI is calculated based on two different interpretations of the load capacity participating in demand side management (DSM) as explained in [8]. To this aim, such capacity is considered either: Flexibility bidden by the CA in the Market Agent, or Maximum technical flexibility capacity according to the characteristics of the clusters The formula of this KPI is the same as in [8], but subindexes have been changed according to the nomenclature used in this document: P DSM = (P DSM) IDE4L (P DSM ) base P peak P DSM represents the amount of load capacity participating in demand side management (DSM) in the base case and the IDE4L case. P peak represents the maximum electricity demand in the area under evaluation. It includes active power losses. The area under evaluation considered for this KPI is the Unareti LV network existent downstream bus number Since CA-related consumers/prosumers connected to this area do not participate in the MA in the base case, (P DSM ) base is zero. On the other hand, (P DSM ) IDE4L is evaluated as the flexibility bidden by the CA in the Market Agent or alternatively as the maximum technical flexibility capacity according to the characteristics of the clusters. 14

15 IDE4L Deliverable 6.3 The time scale considered for the calculation of P DSM and P peak is the same as the activation period of CRP, which also corresponds to the tests duration. B.3 Power Quality and Quality of Supply (line voltage profiles according to EN 50160). B.3.3 Line voltage profiles fulfilling grid nominal voltage requirements (MV and LV) The voltage level of selected network buses (for instance, nodes with low voltage profiles typically located at far-end of radial feeders) is evaluated according to this formula, which is a modification of the KPI B.3.3 Line voltage profiles fulfilling grid nominal voltage requirements : V % = V base V IDE4L V base 100 where V base is the voltage magnitude of the selected bus in the base case and V IDE4L is the voltage of that bus in the IDE4L case. B.2 Reduced energy curtailment of RES and DER Average power and energy curtailed from PV units production is evaluated based on the KPI B.2 Reduced energy curtailment of RES and DER. However, nomenclature has been adapted to WP6 tests according to the following formula: E curtailment = E IDE4L curtailment base E curtailment base E curtailment IDE4L where E curtailment is the average power or energy curtailed during the activated period in the IDE4L case, base and E curtailment is the average power or energy curtailed during the activated period in the base case. Real time performance of CA The RT CA algorithm uses online local measurements of its prosumers facilities to have feedback on their true available flexibility. Then this algorithm reacts to recent delivery performance results to reduce output error of the entire 15-min CRP activation blocks. Furthermore, the algorithm reduces immediate future uncertainty by online discarding prosumers that previously fail in the delivery. In order to assess the benefits of the RT CA algorithm, random flexibility deliveries have been imposed to produce more realistic outcomes and to provide a ground on which the performance of the algorithm can be tested against a contra factual implementation without feedback measurements ( pass-through ). In both cases the following expression applies: P[%] = P delivered P activated where P[%] is the real-time performance of the RT CA, P delivered is the CRP volume delivered to the DSO, and P activated is the CRP volume requested by the DSO (CRP product buyer). Flexibility activated per cluster Consumers/prosumers in CA s portfolio are clustered in three different types according to their devices configuration: (i) prosumers with PV, ESS and HVAC (cluster 1), (ii) prosumers with PV and HVAC (cluster 2) 15

16 IDE4L Deliverable 6.3 and (iii) consumers with HVAC (cluster 3). Since flexibility potential from ESS is different from heating or cooling in HVAC systems, it is expected that their respective contribution to the global volume might be different. The fraction of delivered flexibility corresponding to each cluster is calculated. Congested branch loadings A congestion case occurring in both winter and summer scenarios is line thermal overload. Line loadings are affected by power modulated by network users (such as flexibility deliveries) but also by changes in network configuration (impedances of current paths change and so do power flows). This metric is used to check that activated flexibility fits for technical purpose and takes into account the percentage of the nominal power of the branch analysed in the test case. Power losses in LA The flexibility buyer (DSO) executes the MA to solve a technical issue that arises at MV level. However, resources of its flexibility providers (the CA) are physically connected at LV level. Power losses originate in the LV network connected downstream of the MV/LV substation, and they might have an impact on the effective power modulation that the DSO can use for the congestion problem solution. The following metric has been developed to quantify the effect of power losses on the flexibility delivery at MV level: P CA MV delivered [%] = P MV delivered [kw] CA P delivered CA [kw] P delivered [kw] Active power measurements (PCC, ESS, RES, residential load) In the experimental setup, hardware emulation is used to demonstrate the technical feasibility of some parts of the real-time interactions of the CA with one DER (microgrid) and their integration as an EMS service at lab scale. Furthermore, technical limitations may arise in lab tests which cannot be anticipated with pure simulations prior to field demo implementations, for example, real and concrete specifications and limitations of the devices, such as ESS operation ranges. In this deliverable, active power profiles measured during CRP activation tests are reported, considering both a performing and an underperforming emulated microgrid. 2.3 Setup description Real-Time Commercial Aggregator Algorithm The proposed methodology describes a procedure to allocate a flexibility volume requested by a buyer on demand, as in the case of a DSO trying to solve an unanticipated congestion through flexibility activation. The allocation consists in splitting the requested volume of flexibility among the customers, send the control setpoint to activate the flexibility, check if the requested action has been accepted by all customers and eventually reallocate the flexibility among the participant users. The method assumes that such volume is included in a standard product defined for an activation period of the same length of settlement time periods, i.e. 15, 30 or 60 minutes. This assumption is aligned with the capabilities of smart meters [6]. Such activation period is assumed long enough to allow the acquisition of measurements from the customers, to verify whether the customers are effectively participating to the flexibility and, eventually, to modify the flexibility allocation and to update the flexibility activation signals in real time before the activation period expires. In this flexibility reallocation process, non-responsive customers (e.g. those customers that do not act as planned in the flexibility allocation) are considered not to be available any more during the rest of 16

17 IDE4L Deliverable 6.3 the period, whereas performing users receive new signals with higher amounts of activated volume which compensate for the under-performance of the former. Aggregators manage a diversity of resources but deliver a single flexibility amount at the MV substation per activation period. It is worth noting that the baseline power level (level we would have if we do not activate any flexibility) are defined per settlement time period (i.e. 15, 30 or 60 minutes) in this work, but flexibility volumes (the quantity that is offered) are defined per activation period (e.g. period that the standard product has defined). It is assumed that both periods are not synchronised and baseline values proportional to the flexibility activation time elapsed in two consecutive settlement periods are used instead. In this study, the flexibility activation algorithm is executed in time blocks corresponding to activation periods. Such blocks start when the buyer sends the activation signal to the aggregators operating in certain LA and finishes when the signal ceases and the activation period for that block reaches its end. However, a different period is defined called delivery period which starts when aggregators actually send the remote setpoint signals to their selected providers and ends after the delivery time elapses. The algorithm takes into account a pre-defined latency period between activation and delivery timeslots for aggregators to update their portfolio availabilities for the activated block. In a real implementation such time would be destined for flexibility status requests from aggregators to individual users, as well as the computations to allocate individual volume signals among such users. Main interactions of the RT CA algorithm The RT CA main program interacts either through data flows over socket TCP-IP with the microgrid EMS or through files exchanges (.m,.csv) with the DN simulator. On the other hand, the MA tool developed in WP5 is not used online during the tests but used off-line to produce the inputs for the CRP activated volume. Hence, simply.mat/.csv files (and not programs) are used in the setup to simulate interaction with the MA. The main interactions are depicted on Figure 2.3 and summarized in the following bullets: 1: Order of flexibility activation 2: Status request (baseline + available flexibility) and operational status return 3: Allocation of flexibility volume among DERs proportional to their available amounts 4: DERs activation 5: Activation confirmation 6: Energy measurements (request and return) 7: Modification of flexibility volume target for remaining activation period and re-allocation of set points among DERs 8: Sending of modified control set points More details regarding the RT CA algorithm implementation are provided in Appendix called Detailed Setup Description DN and PCC simulators The simulation of the network and the emulation of the voltage at one of its nodes are done, in IREC lab setup, by the two following algorithms, respectively: Distribution Network simulator 17

18 IDE4L Deliverable 6.3 The DN simulator is the procedure in charge of reflecting the results of the RT CA algorithm decisions on the simulated network, i.e. it modifies the quantities of the grid model according to the temporal evolution of the baselines and activated flexibilities. It calculates the up-to-date grid cases based on the predefined MV and LV profiles and the flexibility provided by the Commercial Aggregator s portfolio. It is also in charge of calculating the Smart Meter measurements from the simulated portfolio customers in order to send them to the CA simulator for compliance checking purposes. Furthermore, an optional consumption deviation from the CA set-point can be applied at this part. Point of Common Coupling simulator This is the algorithm in charge of continuously solving the steady-state operating conditions of the current network case (once the CA s portfolio flexibility has been applied). It also takes into account the flexibilities delivered by other MA participants (MV-connected users) and the local power measurements of the microgrid s PCC (Grid Emulator measurements). By doing this, the voltage level supplied to the microgrid can be emulated in close-to real-time conditions considering the local impact of the measured power exchanged on the voltage magnitude. More details regarding the implementation of DN and PCC algorithms are provided in Appendix called Detailed Setup Description Link of the setup with the KPIs and metrics The different pieces of hardware (HW) and software (SW) involved in the setup are interconnected in order to be able to co-simulate and co-emulate the simultaneous operation of the CA and the real-time interaction with one of the prosumers in its portfolio, as well as the distribution network (DN) and the voltage supply of that prosumer, and the EMS and the power devices and communications of that individual prosumer. Furthermore, some of the pieces of HW and SW have been used for more than one purpose, i.e. not only for the pure execution of the CRP activation test. The most important ones are: Elaboration of scenarios: the grid models included in the DN simulator and customer-related data from the RT CA and DN simulator (type of customer, contracted power, so on) have been used to generate the different scenarios and related cases. The PCC simulator has been used to check for the appearance of constraints (voltage level violations or branch overloadings). The Market Agent tool [10] has also been used off-line to generate the activated CRP volumes in the tests. Configuration of the EMS: the [EMS irems] includes configuration files and programs for its configuration prior to the execution of tests, such as type and nominal ratings of devices, weather and load forecast data inputs, day-ahead energy prices. Configuration of emulators [EMUL CTRL]: the configuration of the active power profiles of the ugrid emulators by means of data files. Taking into account the previous paragraphs, tables and figures, the main pieces of HW and SW contributing to the generation of the KPIs and metrics already defined are listed as follows: 18

19 IDE4L Deliverable 6.3 B.5 Increased flexibility from energy players (DSO). B.5.1 Demand response capability RT CA (flexibility from CA s portfolio: [DER status files]), [MA files] (flexibility bidden in the MA) and DN simulator (peak power values: [DN files]). B.3 Power Quality and Quality of Supply (line voltage profiles according to EN 50160). B.3.3 Line voltage profiles fulfilling grid nominal voltage requirements (MV and LV) DN simulator and PCC simulator (voltages from grid model [DN files] and power flows), voltage measurements at the grid emulator [GE] B.2 Reduced energy curtailment of RES and DER RT CA (PV data from LV customers), DN simulator and PCC simulator (power generated by all PV units in the grid model [DN files]) Real time performance of CA RT CA (total activated flexibility [Activated flex. files]), DN simulator and PCC simulator (flexibility delivery of MV and LV users [energy meas. files], [DN files]) Flexibility activated per cluster RT CA (activated flexibility per cluster [Activated flex. files]), DN simulator and PCC simulator (flexibility delivery of LV users [energy meas. files], [DN files]) Congested branch loadings DN simulator and PCC simulator (power injections from/to nodes [DN files]) Power losses in LA DN simulator and PCC simulator (power losses report after power flow routine execution [DN files]) Active power measurements (PCC, ESS, PV, residential load) voltage and currents measurements of ugrid emulators [AC/DC-DC/AC], 2 nd life battery system [AC/DC-battery] with additional power analyzers Microgrid baseline optimal schedule of ugrid EMS [EMS irems] IREC setup The proposed setup encompasses both simulation and emulation parts in order to simulate and measure magnitudes needed for the KPIs and metrics previously defined. In fact, emulation is used to make practical issues appear during the CRP activation and delivery (that otherwise would probably not be noticed by pure software simulations). In particular, the existence of hardware is useful to assess metrics related to individual DERs/microgrids, such as the performance of individual assets through active power measurements of a PCC, an ESS, a PV unit and residential load. On the other hand, software simulation is used to evaluate power system level KPIs and metrics, such as DSO-related ones ( power quality and quality of supply, congested branch loadings, power losses in LA ). A picture of the setup and the physical model of the setup is shown in Figure 2.1 and Figure 2.2, respectively. The term emulation is used as the hardware simulation of the elements of the microgrid as well as the voltage supply to the PCC of such microgrid. The pieces of hardware and software considered are summarized in 19

20 IDE4L Deliverable 6.3 Table 2-3. Figure 2.1 Pieces of hardware of the setup. 20

21 IDE4L Deliverable 6.3 Figure 2.2 Physical model of IREC s setup for the CA emulation. 21

22 IDE4L Deliverable 6.3 Table Pieces of hardware (rows) and software (columns) of the setup. Numbers in cells indicate the amount of pieces of software. HW [Name in the physical model figure]/sw Real time Commercial Aggregator (RT CA) DN simulator PCC simulator GE remote control system ugrid EMS Configurator ugrid concentrator Local concentrators of ugrid emulators Local concentrator of 2 nd life battery Desktop 1 [Remote simulator] Desktop 2 [EMS irems] 1 Desktop 3 [Configurator + ugrid concentrator] 1 1 Grid emulator power amplifier [GE] Grid emulator embedded PC [GE CTRL] 1 ugrid emulators [AC/DC-DC/AC] 2 nd life battery storage system [AC/DCbattery] Local concentrators of ugrid emulators [EMUL CTRL] and 2 nd life battery [ES CTRL] 2 (1 for PV emulation, 1 for Load emulation) 1 [Switch A] The main algorithms designed and implemented for the tests and their respective interactions are shown in Figure 2.3. The main routines developed in this work are installed in the remote PC ( Remote simulator ) and include the Real-time CA (RT CA) algorithm as well as the DN simulator and the PCC simulator. The programs are run in parallel on MATLAB and their interactions are based either on file exchanges or XML data flows. Those algorithms are briefly described in the next sections. 22

23 IDE4L Deliverable 6.3 Figure 2.3 Main software programs and their interactions RWTH setup For real time power system simulation the Real Time Digital Simulator (RTDS) has been used. The installed RTDS in RWTH laboratory is composed of 8 racks that can accurately and reliably simulate dynamics of power systems generally in the range of 50 µs, which can also be brought down to 2µs in some special cases. The power system of UNARETI is modeled (both LV and MV) in 4 racks of RTDS. Respectively 1 rack for LV (where some customers and generators have been merged in order to save computation power) and 3 racks for the MV grid, which has been modeled one to one with respect to the actual grid. Through its various output boards real time measurement data are made available in analog and digital formats for external devices. It also can send measurement data using the network protocols via Ethernet. For the power profiles real data coming from the DSO have been used as basis for their generation. A collection of real data, acquired during an entire year, has been provided by UNARETI. These data have been combined in order to create the profiles used for the simulation, for both the LV and MV. To create the congestions needed for the emulation of the CA the real data have been recombined and multiplied by different factors, thus obtaining the needed data for the scenarios definition but still based and adherent to the real ones. Thanks to this configuration it was possible to accurately simulate the power system, and test the real time CA algorithms, together with other monitoring applications applied in the IDE4L project. Control signals can be delivered to controllers, such as simulated inverters or intelligent electronic devices, in the simulation 23

24 IDE4L Deliverable 6.3 environment in RTDS with industrial protocols, through emulated communication infrastructure, hence investing also the effects of delays. Figure 2.4 Simulation setup for CA simulation in RWTH Lab The algorithms for the Market Agent and the Commercial Aggregator are run on a dedicate computer in Matlab. The communication between the RTDS simulator and the remote computer is built using the DNP3 protocol available through the GT-Net card of the RTDS simulator and a dedicated OPC server installed on the remote machine. The OPC server, designed using Kepware Kepserver, enables the creation of a channel between the Real-Time simulator and Matlab, which uses its own OPC toolbox for interfacing with such server, thus enabling the exchange of analogue and digital inputs and outputs. These data represent the measurements coming from the simulated grid, such as power and voltage measurements, and the control setpoints sent from the CA to its customers. The complete schema of the test platform is depicted in Figure 2.4. The data exchange between the CA and the MA is made using share matlab files. When congestion is detected the Market Agent computes the needed flexibility and this value is saved into a local file that is later read by the CA. The Commercial Aggregator receives the CRP activation request and the flexibility required by the MA and then starts its allocation algorithm. 24

25 IDE4L Deliverable 6.3 The MA receives data from the simulation regarding power and voltages in the MV network, checks if there is a congestion and, in case it detects a congestion, runs an Optimal Power Flow algorithm to find the amount of flexibility needed to resolve it. The CA, when receiving a CRP activation request, enters in the main loop for the flexibility allocation. During this loop, that lasts 15 minutes, there a continuous exchange of data from and to the Real-Time simulator, in order to acquire the data regarding the actual status of the LV grid and to send the control setpoints to the customers to allocate the flexibility, check whether the controls have been received and executed by the customers and eventually recalculate the setpoints according to the real participation of the users. 2.4 Test Scenarios Two different test scenarios have been adopted in order to demonstrate the real-time performance of the CA with both downwards ( load curtailment ) and upwards ( load increase )flexibility volumes requested by the flexibility buyer. In both cases the flexibility volume requested is used to contribute to the suppression of MV branch congestions. On one hand, in the winter scenario the CRP activation of load curtailment ( downwards flexibility ) is requested by the MA. On the other hand, in the summer scenario the activated CRP product corresponds to load increase ( upwards flexibility ) as an alternative to conventional PV curtailment. Both scenarios have been built ad hoc for the test execution and do not represent a specific forecasted scenario. On the other hand, a methodology has been designed based on data extracted from a real distribution network (Unareti MV and LV network) and a set of real domestic customers. Based on this real-world data, the two scenarios have been designed so as to generate a congestion level large enough to require a significant amount of flexibility from both one CA and two additional MV network users. The tools used to originate congestion have included the scaling up of load profiles (for the winter scenario) and a higher PV penetration (for the summer scenario). The network model shown in Figure 2.5 and Figure 2.6 is the outcome of the aforementioned methodology. It is worth noting that the prosumer emulated in the laboratory is a microgrid with PV unit and ESS connected to the LV feeder 3 in an intermediate location. 25

26 IDE4L Deliverable 6.3 Figure 2.5 MV network used in the test scenarios. Figure LV network used in the test scenarios. Already defined KPIs and some metrics imply the comparison of a case where the IDE4L architecture is simulated/emulated (so-called IDE4L case ) against a case where business as usual (BaU) methodologies or conventional congestion management tools are used. In this work, those cases are called base cases. 26

27 IDE4L Deliverable 6.3 For the winter scenario, the base case is related to load curtailment of relatively large MV network users, assuming no CA is ready to activate flexibility. For the summer scenario, the base case assumes the DSO applies PV curtailment of both MV and LV-connected PV units. Furthermore, in the winter scenario, the IDE4L case has been tested both with performing and underperforming emulated microgrid in order to validate the RT CA algorithm implementation within the experimental setup. Table 2-4 Main differences among scenarios and cases. Winter scenario (id 1) Summer scenario (id 2) Mid-Season Scenario (id 3) Base case CA No No No DSO Market Agent PV curtailment Market Agent Curtailed users MV users MV users + LV users MV users IDE4L case CA Yes Yes Yes DSO Market Agent Load increase Market Agent Curtailed users MV users + aggregated LV users MV users + aggregated LV users MV users + aggregated LV users Performing and nonperforming emulated microgrid Performing emulated microgrid In the following subsections, both scenarios are schematically described as well as their link to the emulation goals and KPIs and metrics. For further details on the assumptions and methodology to generate the scenarios, please refer to Appendix Detailed Scenarios Description Winter scenario tests TEST SCENARIO INFORMATION Scenario Name CRP Activation (Winter) Scenario ID 1 Main Objective Scenario Description Quantification of the amount of electrical power (load) that can be modulated to the needs of the system operation within a specified unit of time in the base case compared to the additional flexibility that is delivered by Commercial Aggregators (IDE4L case). UNARETI network (MV and LV). High penetration levels of PV (both MV LV levels) and distributed energy storage systems (ESS). A fraction of PV and ESS units are locally managed by means of residential microgrids. Winter day with overloaded MV branch. Sequence of events (IDE4L congestion management scheme): 1. DSO s Tertiary Controller (TC) runs the Market Agent (MA) because it cannot remove a constraint in the MV branch between the substation (SS) MV busbar and bus 545, Figure 2.5. Such constraint arose due to a previous Network Reconfiguration (NR) that consisted in the opening of the switch (SW) located 27

28 IDE4L Deliverable 6.3 Connected KPIs Connected Additional Findings (Metrics) Emulation steps Input data preparation Hardware/softwar e configuration Test execution Data postprocessing Emulation steps Input data preparation Hardware/softwar e configuration Test execution upstream feeder E23L03 and the closing of SW downstream the same feeder. 2. [BASE CASE] The optimal solution from the MA is flexibility (CRP product) activated by two MV load units. No CAs participate in the MA. [IDE4L CASE] The optimal solution from the MA is flexibility (CRP product) activated by two MV load units and one CA. 3. [BASE CASE] The CRP activation process takes place. [IDE4L CASE] The CRP activation process takes place. The CA runs the RT CA algorithm. 4. Before the CRP activation period expires, the initial NR causing congestion is removed and no additional CRP activations are needed. 5. The system can be finally operated without NR and CRP activation. B.5 Increased flexibility from energy players (DSO): demand response capability of the system is increased with the contribution from Aggregators B.3 Power Quality and Quality of Supply (line voltage profiles): side-effects on bus voltages of the flexibility allocated among LV network users Real time performance of CA: individual customers/prosumers in CA s portfolio may fail in the CRP delivery thus affecting the global performance Flexibility activated per cluster: according to the cluster they belong to, demand response capability of consumers/prosumers may vary (ESS, heating) Congested branch loadings: overloading level of branches depend on power modulated (flexibility) and network configuration (NR) Power losses in LA: power losses in the LV network have an effect on the aggregated curtailed volume delivered at the corresponding MV node Active power measurements (microgrid): power flows exchanged in PCCs with the DN are the summation of flows from individual microgrid devices (ESS, PV, HVAC, residential load) TEST BASE CASE EXECUTION METHODOLOGY Step MA runs without participation of Commercial Aggregator (only MV network units bid and offer). CA module deactivated. Configuration of residential load profiles and PV profiles in emulators. Configuration of EMS (energy arbitrage): electric energy prices, weather and load forecasts. Run test with the same load and PV profiles in emulators configuration and EMS s configuration. Data collection, analysis and calculation of KPIs value. Analysis of additional findings (metrics). TEST IDE4L CASE EXECUTION METHODOLOGY Step MA runs with participation of Commercial Aggregator. CA module activated. Configuration of residential load profiles and PV profiles in emulators. Configuration of EMS (energy arbitrage): electric energy prices, weather and load forecasts. Run test: First, with the same load and PV profiles in emulators configuration and EMS s configuration. Second, with different load and PV profiles in emulators configuration and EMS s configuration. 28

29 Data postprocessing Data Active power measurements (microgrid): battery, PV, load (kw) Customer/prosumer data and network simulation data IDE4L Deliverable 6.3 Data collection, analysis and calculation of KPIs value. Analysis of additional findings (metrics). EMULATION DATA COLLECTION Dat a ID Methodology for data collection Data logging in setup devices and additional measure ment equipmen t Simulatio n files saving in remote simulator Source/Tools/Instrument s for Data collection Local [EMUL CTRL], [ES CTRL]and ugrid concentrator Yokogawa WT1600 and WT500 power analyzers RT CA, DN simulator, PCC simulator csv, Excel, Matlab, Matpower files Location of Data collection Desktop 3 [Configur ator + ugrid concentr ator] Yokogaw a WT1600 and WT500 power analyzers Desktop 1 [Remote simulator ] Frequency of data collection 1 s (ugrid concent rator) 100 ms / 50 ms (Yokoga wa) Twice every 15min (RT CA) 1min (DN simulat or) 3s (PCC simulat or) GENERAL COMMENTS Test chronology: 19:00 NR, CRP just activated but non-delivered flexibility yet Congestion 19:06 NR, CRP activated and delivered flexibility (first setpoints allocation) No congestion 19:13 NR, CRP activated and delivered flexibility (setpoints reallocation) No congestion 19:15 No NR, CRP just deactivated but still delivered flexibility (setpoints reallocation) No congestion 19:21 No NR, CRP deactivated and just stopped delivered flexibility No congestion Minimum monitorin g period 30 min 30 min Summer scenario tests TEST SCENARIO INFORMATION Scenario Name CRP Activation (Summer) Scenario ID 2 Main Objective Scenario Description Quantification of the amount of energy output from RES that should be reduced due to technical reasons in the base case but can be tolerated with the Commercial Aggregator implementation (IDE4L case). UNARETI network (MV and LV). High penetration levels of PV (both MV LV levels) and distributed energy storage systems (ESS). A fraction of PV and ESS units are locally managed by means of residential microgrids. Sunny summer day with low loaded distribution network and high reverse flow in MV branch. The strategy to reduce such reverse flow is different in the base case and the IDE4L case: [BASE CASE]: PV is curtailed at MV PV plants as well as LV PV units, in a way similar to existing conventional PV curtailment schemes in pre-emergency grid conditions (such as in Germany). In this case PV is curtailed from the MV PV unit connected to bus 1056 and the LV PV units connected downstream

30 IDE4L Deliverable 6.3 Connected KPIs Connected Additional Findings (Metrics) Emulation steps Input data preparation Hardware/softwar e configuration Test execution Data postprocessing Emulation steps Input data preparation Hardware/softwar e configuration Test execution Data postprocessing Data Active power measurements (microgrid): battery, PV, [IDE4L CASE]: ad hoc assumption that flexible MV and LV load is increased in order to avoid PV curtailment. The MA does not allow load increments be traded, thus the activated flexibility volume per network user is allocated manually. In this case, load is increased in MV users connected to buses 297 and 1006, as well as aggregated LV users connected downstream bus Sequence of events: 1. The DSO cannot remove a constraint in the MV branch connecting the SS MV busbar and bus 1056, Figure 2.5. Such constraint arose due to a previous Network Reconfiguration (NR) that consisted of the opening of SW located upstream of the feeder E23L01 and the closing of SW downstream E23L03 feeder. 2. [BASE CASE] The DSO curtails PV at MV and LV levels. [IDE4L CASE] The DSO increases load at MV level and aggregated LV level (CA). The CA runs the RT CA algorithm. 3. Before the PV curtailment/load increase period expires, the initial NR causing congestion is removed and no additional PV curtailment/load increase is needed. 4. The system can be finally operated without NR and CRP activation. B.2 Reduced energy curtailment of RES and DER (DSO) Active power measurements (microgrid): power flows exchanged in PCCs with the DN are the summation of flows from individual microgrid devices (ESS, PV, HVAC, residential load) TEST BASE CASE EXECUTION METHODOLOGY Step RES curtailment calculation. No MA. CA module deactivated. Configuration of conventional load, PV profiles in emulators. Configuration of EMS (energy arbitrage): electric energy prices, weather and load forecasts. Run test with the same load and PV profiles in emulators configuration and EMS s configuration. Data collection, analysis and calculation of KPI value. TEST IDE4L CASE EXECUTION METHODOLOGY Step Load increase calculation. No MA. CA module activated. Configuration of conventional load, PV profiles in emulators. Configuration of EMS (energy arbitrage): electric energy prices, weather and load forecasts. Run test with the same load and PV profiles in emulators configuration and EMS s configuration. Data collection, analysis and calculation of KPI value. Dat a ID Methodology for data collection Data logging in setup EMULATION DATA COLLECTION Source/Tools/Instrument s for Data collection Local [EMUL CTRL], [ES CTRL]and ugrid concentrator Location of Data collection Desktop 3 [Configur Frequency of data collection 1 s (ugrid concent Minimum monitorin g period 30 min 30

31 IDE4L Deliverable 6.3 load (kw) Customer/prosumer data and network simulation data devices and additional measure ment equipmen t Simulatio n files saving in remote simulator Yokogawa WT1600 and WT500 power analyzers RT CA, DN simulator, PCC simulator csv, Excel, Matlab, Matpower files ator + ugrid concentr ator] Yokogaw a WT1600 and WT500 power analyzers Desktop 1 [Remote simulator ] rator) 100 ms / 50 ms (Yokoga wa) Twice every 15min (RT CA) 1min (DN simulat or) 3s (PCC simulat or) GENERAL COMMENTS Test chronology: 13:00 NR, PV curtailment [BASE CASE] or Load increase [IDE4L CASE] just activated but nondelivered yet 13:06 NR, PV curtailment [BASE CASE] or Load increase [IDE4L CASE] activated and delivered flexibility (first setpoints allocation) 13:13 NR, PV curtailment [BASE CASE] or Load increase [IDE4L CASE] activated and delivered flexibility (setpoints reallocation) 13:15 No NR, PV curtailment [BASE CASE] or Load increase [IDE4L CASE] just deactivated but still delivered flexibility (setpoints reallocation) 13:21 No NR, PV curtailment [BASE CASE] or Load increase [IDE4L CASE] deactivated and just stopped delivered flexibility 30 min Congestion No congestion No congestion No congestion No congestion Mid-Season scenario tests TEST SCENARIO INFORMATION Scenario Name CRP Activation (Mid-Season) Scenario ID 3 Main Objective Scenario Description Connected Additional Findings Robustness of the CA algorithm with respect to partial or no participation of customers to the CRP activation. UNARETI network (MV and LV). Real value for penetration levels of PV (both MV LV levels) and distributed energy storage systems. Mid-Season morning with overloaded MV branch. Sequence of events (IDE4L congestion management scheme): 1. DSO s Tertiary Controller (TC) runs the Market Agent (MA) because it cannot remove a constraint in the MV branch between buses 952 and 1056, Figure The optimal solution from the MA is flexibility (CRP product) to be activated by the CA. 3. The CRP activation process takes place. The CA runs the RT CA algorithm. 4. After the CRP activation window there is no congestion present and the system can be operated without CRP activation. Reallocation of flexibility in real time 31

32 IDE4L Deliverable 6.3 (Metrics) Emulation steps Input data preparation Hardware/software configuration Test execution Data postprocessing Data Active power measurements (kw) Customer/prosumer data and network simulation data TEST IDE4L CASE EXECUTION METHODOLOGY Step MA runs with participation of Commercial Aggregator. CA module activated. Configuration of residential load profiles and PV profiles in emulators. Run test: Allocation of flexibility among customers. Verification of participation to proposed flexibility Reallocation of flexibility only among following the proposed CRP activation Data collection, analysis and calculation of KPIs value. Analysis of additional findings (metrics). EMULATION DATA COLLECTION Data ID Methodology for data collection Data logging in setup devices Data logging in setup devices and simulatio n files saving in remote simulator Source/Tools/Instruments for Data collection Real-Time simulator values transmitted through the DNP3 communication protocol to the MA and CA RT CA, MA, RTDS simulator Csv, Matlab, Matpower files Location of Data collection Desktop running the CA and MA algorithm s Database for data storage Desktop 1 [Remote simulator ] Frequency of data collection 200 µs (RTDS simul ation time step) Twice every 15mi n (RT CA) GENERAL COMMENTS Test chronology: 07:00 CRP just activated but non-delivered flexibility yet Congestion 07:06 CRP activated and delivered flexibility (first setpoints allocation) No congestion 07:13 CRP activated and delivered flexibility (setpoints reallocation) No congestion 07:15 CRP just deactivated but still delivered flexibility (setpoints reallocation) No congestion 07:21 CRP deactivated and just stopped delivered flexibility No congestion Minimum monitoring period 30 min 30 min 2.5 Simulation results The analysis of results of the simulation/emulation tests for the different cases defined in the winter and summer scenarios has been organised per KPIs and metrics. The winter scenario has been analysed in more detail, whereas the summer scenario has been studied from the point of view of two specific KPIs/metrics, which are B.2 Reduced energy curtailment of RES and DER and Active power measurements (PCC, ESS, RES, residential load). KPIs (winter scenario) 32

33 IDE4L Deliverable 6.3 B.5 Increased flexibility from energy players (DSO). Demand response capability Flexibility bidden by the CA in the Market Agent Maximum technical flexibility capacity according to the characteristics of the clusters P DSM 26% 81% According to the assumptions used to generate the two winter scenario cases (base and IDE4L), demand response capability at low voltage level may increase significantly with the activation of flexibility from CAs. Since in the base case no flexibility was available, these results directly indicate the percentage of the peak demand that can be curtailed in the CRP activation use case. The value on the right is a theoretical maximum considering availability of all ESSs at full power for the activation period as well as flexibility sourced from HVAC (heating). On the other hand, the value on the left is a fraction of the former (roughly 1/3), since it just considers the volume bidden by the CA in the Market Agent. The following remark should be noted about the aforementioned results: the flexibility bidden by the CA in the MA is an amount resulting from the winter scenario development. According to the maximum technical flexibility capacity from consumers/prosumers, P DSM could have a value ranging from 0% to 81%. This 26% figure is the outcome of this particular scenario. Original real customers profiles needed to be scaled up by a factor of 411/261 in order to produce significant congestion in a MV branch for the CRP activation use case. Authors think that assumptions used in the scenario generation methodology are reasonable and originally based on real data, thus P DSM results indicate that such peak load increase may be at least partially accommodated in the power system with the contribution of the 0%-81% flexibility. B.3 Power Quality and Quality of Supply (line voltage profiles according to EN 50160). Line voltage profiles fulfilling grid nominal voltage requirements The voltage magnitudes of the MV and LV buses with lowest voltage profile are shown in the following tables. Additionally, the bus emulated with the PCC simulator (# ) is shown. Table 2-5 Lowest MV voltage profiles (winter scenario) Simulation interval Base Bus # with lowest V profile (MV) IDE4L Bus # with lowest V profile (MV) V% bus with lowest V profile (MV) 19:00 NR, CRP just activated but non-delivered flexibility yet 19:06 NR, CRP activated and delivered flexibility (first setpoints allocation) 19:13 NR, CRP activated and delivered flexibility (setpoints reallocation) % % % 33

34 IDE4L Deliverable :15 No NR, CRP just deactivated but still delivered flexibility (setpoints reallocation) 19:21 No NR, CRP deactivated and just stopped delivered flexibility % % Table Lowest LV voltage profiles (winter scenario) Simulation interval Base Bus # with lowest V profile (MV) IDE4L Bus # with lowest V profile (MV) V% bus with lowest V profile (MV) 19:00 NR, CRP just activated but non-delivered flexibility yet 19:06 NR, CRP activated and delivered flexibility (first setpoints allocation) 19:13 NR, CRP activated and delivered flexibility (setpoints reallocation) 19:15 No NR, CRP just deactivated but still delivered flexibility (setpoints reallocation) 19:21 No NR, CRP deactivated and just stopped delivered flexibility % % % % % Table 2-7 Emulated node LV voltage profiles (winter scenario) Simulation interval Base Bus # V (LV) IDE4L Bus # V (LV) V% emulated bus IDE4L locally performing (LV) 19:00 NR, CRP just activated but non-delivered flexibility yet 19:06 NR, CRP activated and delivered flexibility (first setpoints allocation) 19:13 NR, CRP activated and delivered flexibility (setpoints reallocation) 19:15 No NR, CRP just deactivated but still delivered flexibility (setpoints reallocation) 19:21 No NR, CRP deactivated and just stopped delivered flexibility % % % % % 34

35 IDE4L Deliverable 6.3 V % KPI improves in all cases (MV and LV bus with lowest voltage magnitudes as well as emulated LV bus) since it is a negative value (i.e. the voltage level increases in the IDE4L case compared to the base case). Hence, a first finding is that power curtailment not only relieves branch overloads but also improves voltage levels in this case. This beneficial result from the flexibility activation is dependent on the location and proximity of flexibility resources to the measured node. As an illustration, in the IDE4L case flexibility is not only sourced at MV level but also at LV level, thus generating greater beneficial local effects at LV level (voltage increases up to 0.7% in LV bus whereas it just increases up to 0.04% in 1056 MV bus). Metrics (winter scenario) Real time performance of CA Pass-through RT CA implementation IDE4L RT CA implementation P[%] 110.3% 105.4% With the IDE4L RT CA implementation, i.e. with reallocation after online energy measurements, the RT CA reduces CRP over-performance by -4.4%. This result may be understood as an improvement in efficiency of flexibility delivery since the extra 10.3% of flexibility is not needed by the DSO to remove the congestion. Please note that it is assumed that individual performance of consumers/prosumers is kept constant throughout the delivery period. Further analysis would be needed to define methodologies to adapt the algorithm to variable temporal individual performances. Flexibility activated per cluster Cluster 1 (flex ESS + HVAC) PV Cluster 2 (flex HVAC) PV Cluster 3 (flex HVAC) no PV Global per cluster 90.2% 3.3% 6.5% Average consumer/prosumer cluster 0.90% 0.10% 0.16% Results show that ESSs are significantly contributing to the overall flexibility amount (90.2%). Since these figures are closely related to the generated winter scenario which is based on real-life and realistic assumptions, it is highlighted that certain types of flexibility resources might be more convenient than others to be readily valuable for the system. As an illustration, the individual contribution from ESS in this scenario is several times greater than the contribution from heating. Congested branch loadings The loading profile of the line connecting SS MV busbar and bus 545 is shown in Figure 2.7. It is observed that initially the branch is overloaded below 110%, and overloading disappears at minute 00:06 (when flexibility is delivered to the system). Later on, the grid is configured back to the usual operating status of 35

36 IDE4L Deliverable 6.3 the SWs (00:15), and a significant part of the load is transferred to feeder E23L03, thus alleviating this line that is located in feeder E23L01. Finally, at instant 00:21 no flexibility is delivered anymore and the load in this line slightly increases. Figure 2.7 Congested branch loading (winter scenario, IDE4L case). Power losses in LA The time plot of active power losses in UNARETI LV network has the same shape (of course with significantly lower values) as the active power demand, see Figure 2.8 and Figure 2.9, respectively. In fact, losses do only vary when there is a change in the activated flexibility volume (00:06, 00:13 and 00:21) or when load baselines change (00:00 and 00:15). When load consumed/generated at each LV node changes, power flows also do vary and so do power losses. In this case, the impact of losses on the delivery of energy curtailment at MV level (bus 1056) is beneficial for the congestion solving process because an amount of curtailment is added to the aggregation of individual load curtailments. In particular, the power curtailment increase P CA MV delivered [%] is of 1.8%. CA MV P delivered [kw] P delivered [kw] P CA MV delivered [%]

37 IDE4L Deliverable 6.3 Figure 2.8 Active power losses in LV network downstream bus 1056 (winter scenario, IDE4L case). Figure Active power demand in LV network downstream bus 1056 (winter scenario, IDE4L case). Active power measurements (PCC, ESS, RES, residential load) 37

38 IDE4L Deliverable 6.3 In the winter scenario, the following sequence of events is the outcome of the IDE4L case for the emulated microgrid: 1. The microgrid s EMS sends (Baseline; controllable power)= (0kW; -1,68kW) available at microgrid s Point of Common Coupling to the RT CA. 2. The RT CA sends initial -0,54kW flexibility control setpoint to the EMS. 3. Two sub-cases are demonstrated: a. Performing microgrid (-0,53kW setpoint after flexibility reallocation) b. Under-performing microgrid (wrong flexibility forecast causes excessive output error at the PCC, 0kW after flexibility reallocation) Figure 2.10 Active power profiles of microgrid s devices (winter scenario, IDE4L case, performing microgrid). 38

39 IDE4L Deliverable 6.3 Figure Active power profiles of microgrid s devices (winter scenario, IDE4L case, under-performing microgrid). In the sub-case with performing microgrid, measurements were realised with Yokogawa WT1600 and WT500 power analyzers (frequency of data collection of 50 ms and 100 ms, respectively), whereas in the sub-case with under-performing microgrid measurements were obtained from logs in local concentrators [EMUL CTRL], [ES CTRL] and ugrid concentrator (frequency of data collection of 1000ms). In both performing and under-performing sub-cases, the RT CA sends an initial -0,54 kw flexibility control setpoint to the EMS, but in the second sub-case the residential load unexpectedly increases up to 4 kw. One minute after the load increase takes place, the excessive power is supplied from the battery as a consequence of the action of the EMS. The battery output power has reached its technical limit in this situation and thus no more flexibility (additional battery discharge amount) can be delivered to the CA. In the second sub-case the microgrid becomes non-eligible for the rest of activation period (00:13) but in the first sub-case receives a second setpoint as a consequence of the reallocation process taking place in the RT CA. Finally, it is worth noting that power spikes are observed in both sub-cases in instants (00:06, 00:15 and 00:21). The origin of these spikes is the different voltage level changes that are caused by the PCC simulator in the grid emulator (GE) when nodal active power values change (baseline or activated flexibilities). KPI (summer scenario) B.2 Reduced energy curtailment of RES and DER 39

40 IDE4L Deliverable 6.3 Expressed in kw (kwh) Expressed in % E curtailment -263 (-65.75) -100% Results show that, on average, up to 263 kw of extra PV injection can be absorbed for 15 minutes in the IDE4L case also keeping acceptable grid operating conditions at the same time. This is an equivalent reduction of 100% of PV curtailment. This outcome is a comparison between two opposite extreme cases, i.e. only PV curtailment in the base case and only load increase in the IDE4L case. Additionally, the fraction of PV power curtailed P curtailment over the PV power injected to the system P PV in order to remove constraints in the base case has been calculated to illustrate the RES penetration of the summer scenario: UNARETI MV + LV networks Aggregated LV PV MV PV P curtailment 2.5% 24.0% 55.6% P PV A message that can be extracted from this particular case is that conventional RES curtailment applied in congestion management schemes can be virtually 100% avoided in scenarios with significant penetration of resources with upwards flexibility capability. ESSs in the distribution network are assumed as the most significant contributor to this type of capability. Since ESSs (home batteries, EVs) are most times connected to LV nodes, Aggregators have a means to enhance flexibility potential of the system with such ESSs. Metrics (summer scenario) Active power measurements (PCC, ESS, RES, residential load) In this case active power measurements are shown as proof of the technical feasibility of upwards flexibility locally delivered by DERs. In the summer scenario, flexibility delivered by simulated resources is assumed without output error, thus no corrections occur in the reallocation routine (the setpoint sent to the emulated microgrid is kept the same as in the first allocation). 40

41 IDE4L Deliverable 6.3 Figure Active power profiles of microgrid s devices (summer scenario, IDE4L case). Metrics (Mid-season scenario) In this scenario the goal was to evaluate how the Real-Time execution of the CA algorithms could adapt to partial acceptance of proposed flexibility from the customers. The main advantage of the Real-Time execution, in fact, is that the algorithm can verify how the users react to the proposed commands and change the allocation strategy accordingly, in order to deliver the requested CRP products even if only a small group of customers participate to the flexibility allocation. When the MA identifies a congestion, sends a request of CRP activation to the CA with the requested amount of flexibility needed to solve the congestion. In the teste scenario a congestion of % is found in the line between buses 952 and The MA identifies the needed flexibility to solve the congestion and send a request to the CA. The flexibility requested during the simulation was 25,8 kw during the 15 minutes time window. Once the CRP activation request is received the CA divides the total flexibility among its customers and, after 6 minutes from the start of the simulation, sends the first setpoints. In Figure 2.13 the profile of active power for bus 1056 is reported. It can be noted that, around minute 6 of the simulation the profile decreases, even if slightly, due to the first setpoint command execution from the customers. However the customers do no respond as expected, since many of them do not participate to the CRP activation as requested from the CA. Hence the CA recalculates the setpoint, at minute 13 of the simulation, taking into account the real participation of the customers to the flexibility and discarding those who did not execute the requested commands. 41

42 IDE4L Deliverable 6.3 After the reallocation of the flexibility, around minute 15, the CA sends new setpoint to the customers who are actually participating to the CRP activation, this obtaining a considerable decrease in the power profile of the bus. The reallocation of the flexibility at this stage takes into account not only the original amount, but also the flexibility not delivered during the first part of the allocation. For the remaining part of the allocation window the involved customers participate to the flexibility, until the end of the 15 minutes time window. In the meantime the MA has verified that the original congestion is solved, hence no more CRP products are needed. The CA continue to deliver the CRP until the desired duration of 15 minutes has been reached and then send the final command stopping the flexibility delivery, around minute 21. Figure 2.13 Active Power profile for bus 1056 during CRP activation 2.6 Global interpretation of simulation results The main goal and challenge addressed is summarized in the sentence how does a Commercial Aggregator manage to allocate flexibility among DERs?. In real life, DERs may fail in the delivery of flexibility to the power system, thus some of them will under/over-perform. Then another question arises, in case of under/over-performance of the aggregated flexibility, how can a Commercial Aggregator better adjust the energy delivered during the actual activation time slot?. Results obtained are summarized as follows: Two different sources of demand side flexibility have been considered: ESSs (home batteries) and HVAC (heating in the winter scenario and cooling in the summer scenario). Results suggest that ESSs in domestic environments have higher flexibility potential in terms of average power injected/absorbed to/from the grid. 42

43 IDE4L Deliverable 6.3 Advanced prosumers managing EMSs typically will use ESSs in a coordinated way with other devices at their premises in order to optimize local operating costs. Interaction of EMSs with CAs adds complexity to the management of ESSs. Tests have revealed that DERs may fail in delivering the activated volume in cases where ESSs are used to comply with local optimization programs restrictions, specifically when there have been errors in forecasted power profiles. ESSs have been also proposed as a means to accommodate additional PV power into the system and avoiding related congestion at the same time. Again, the same comment of the previous bullet also applies in this case. The efficiency of flexibility delivery of pass-through CA methodologies can be improved including real-time measurements as feedback for the RT CA. From a DSO s point of view, congestions consisting of branch thermal overloadings have been eliminated via CRP activation. Positive side-effects of power curtailment are also voltage level improvements in locations close to the nodes where power is curtailed. DSO s NR and MA tools need be coordinated since branch overloadings are affected by changes in network configuration as well as changes in power injections/withdrawals. And last, but not least, with the participation of CAs, congestion at MV level is also eliminated through power modulated at LV level. Active power losses within the corresponding LV network where power is modulated further increase the amount effectively curtailed at the MV node of the MV/LV substation since losses might get also curtailed. 3 DYNAMIC TARIFF (DT) 3.1 Simulation goals The main goal of this emulation is to test the DT method, as a price-based DR program, for congestion management in a near real-life environment. The IDE4L project has developed and demonstrateed the control and management of active distribution networks with high penetration of DERs. The DT method is very efficient for congestion management in active distribution networks as has been studied in WP5. However, the DCOPF method and offline simulations are employed in the study for WP5. Therefore, it is very important to test the effectiveness of the DT method in a near real-life environment. This is to be done in WP6 through the real-time simulation equipment, namely the real-time digital simulator (RTDS) Simulation goal 1 The first simulation goal is to test the convergence of the centralized and distributed optimization in an environment close to real life. The rates of the DT are determined at the DSO side using the DC OPF method. Then the DT is published to all the aggregators and they will make their own optimal schedule of the DERs. The test is to check whether the consumption/load profile resulting from the optimal planning of the DSO matches the one from the aggregators, which is measured from the RTDS system. The KPI for this simulation goal is the line loading [%]. The KPI will be compared for the optimal planning at the DSO side and the one from the results of the RTDS emulation, which is resulting from the planning of all the aggregators in the simulated distribution network. 43

44 IDE4L Deliverable Simulation goal 2 The second simulation goal is test the effectiveness of the price signals determined by the DC OPF in the AC power system. With the DT method, the aggregators plan their DER through an optimization considering the energy cost and the received DT rates. It is expected that the congestion can be alleviated after using the DT method comparing to without using the DT method. The KPI for this simulation goal is the reduction of peak load. The peak load of the scenario with the DT method and the scenario without the DT method is compared and hereby the reduction of peak load is calculated. 3.2 Setup description The real time digital testing platform created in this emulation contains a distribution network representation (see Figure 3.1) implemented in the RTDS, an OPC server which handles the communication of DTs from DSO to aggregators and energy plans from aggregators to the RTDS. Inside the real time digital testing platform, a DSO client is implemented with a Matlab and a GAMS script which are used to perform the day-ahead dynamic tariff calculation. Likewise, two aggregator clients are implemented which performs the optimization for their own DER planning for the day-ahead dynamic tariff method through a Matlab and a GAMS script. Figure 3.1 Topology of 10kV distribution network connected to the NOR 60 kv bus in the city of Rønne in Denmark, implemented in the real time digital testing platform for online tests 44

45 IDE4L Deliverable 6.3 The OPC server, DSO client and aggregator clients are implemented separately on different computers to emulate how the day-ahead dynamic tariff would work if it was implemented in a real power system. The communication links of the real time digital testing platform are shown in Figure 3.2 together with the protocols used for each communication link. Figure 3.2: Layout of the digital real time testing platform. The communication links between clients in the online testing environment are established by the MatrikonOPC DNP3 server which enables a communication between an OPC protocol used by Matlab and a Distributed Network Protocol (DNP3) used by the RTDS. In Appendix 1, the purpose and implementation of each client in Figure 3.2 are described in greater detail. 3.3 Test scenarios In order to achieve the simulation goals, i.e. demonstrate the convergence of the DT algorithm and the efficacy of the DT method in a near real-life environment, three test cases were designed. Each test case has two scenarios: the DER planning with the DT method and the DER planning without the DT method Test Case 1 For the first test case, 25% of the houses have electrified their services meaning 696 EVs and HPs are distributed within the distribution network. For the two scenarios, the simulation is performed using the algorithm flow of the real time digital testing platform shown in Figure 3.3Error! Reference source not found.. The real time digital testing platform is initialized by predictions performed by the DSO and aggregators. In the online tests performed in this report the predicted data are assumed equal to the predicted data used for offline tests. After the data used in the day-ahead dynamic tariff method are predicted, these data are sent to the DSO and the aggregators in the distribution network. When the DSO receives predicted data, it performs the day-ahead dynamic tariff optimization. After the DSO has found the optimal solution, the DTs are calculated and sent to the aggregators. Each aggregator uses the predicted data and calculated DTs to optimize the operation of their subscribed DERs through the optimization problem. The aggregators use the optimal solution of the optimization problem as the energy plans of subscribed DERs and post the combined power consumption from the energy plans to the day-ahead electricity market. 45

46 IDE4L Deliverable 6.3 As the energy plans only contain information about active power consumption, the online tests of the distribution network is initialized by calculating the reactive power consumption at each bus in each hour of operation. The reactive power consumption Q is found from the active power consumption P with a predefined power factor. When the active and reactive power consumption from base loads and DERs at each bus is found, the distribution network of Rønne is simulated in the RTDS using a software tool called RSCAD. In RSCAD, the distribution network is modelled and the resulting power system stability parameters are shown as a result of simulations in the RTDS. Figure 3.3. Algorithm workflow of the day-ahead dynamic tariff method implemented in the real time digital testing platform 46

47 IDE4L Deliverable Test case 2 In this case study, the line current loading, bus voltage magnitudes and transformer loading are investigated in the distribution network with an increased penetration of DERs from 25% to 50% Test case 3 In this test case, the implementation of DERs is increased from 50% penetration to 75% penetration. 3.4 Simulation results Test case 1 In the first test case, 25% of the households have EVs and HPs. In order to evaluate the efficiency of the day-ahead dynamic tariff method, first the imported and overall consumed active and reactive power in the distribution network is shown for the two scenarios in Figure 3.4Error! Reference source not found.. Figure 3.4. External grid active and reactive power imported to the distribution network compared to the combined active and reactive power consumption of distribution network loads with a penetration level of 25% DERs. The left side represents the distribution network simulations with the day-ahead dynamic tariff method activated and the right side represents the simulations without the method activated. By comparing the two top plots in Figure 3.4, it is apparent that without the day-ahead dynamic tariff method implemented, the consumption and import of active power is higher in hour 4 than for the system with the day-ahead dynamic tariff method activated. The network without the method implemented imports 120kW and consumes 118kW more than the network with the method implemented. As the DTs in this hour, for feeder 2 and this level of penetration in Figure 3.4Figure 3.3, is larger than zero, the method 47

48 IDE4L Deliverable 6.3 tries to alleviate congestion by lowering the active power consumption in this hour and shifts the load to other hours of the day, which explains the difference between the two scenarios shown in the top of Figure 3.4. In the top plots of Figure 3.4, the active power imported to the distribution network through the external grid is almost equal to the consumed active power in the network in both scenarios. The difference between imported and consumed active power is due to active power losses in the distribution network. In the distribution network implemented in the real time digital testing platform, there are two types of power system equipment that contribute to active power losses, transmission lines and transformers. In the bottom plots of Figure 3.4, the reactive power imported from the external grid and the consumed reactive power in the load buses in the distribution network are shown for both scenarios. All the loads in the network have lagging power factor and so does the implemented HPs. As loads are inductive they consume reactive power and require an import of reactive power from the external grid as shown in Figure 3.4. A comparison of the imported and consumed reactive power in the network with and without the dayahead dynamic tariff method implemented shows that in hour 4, the network without the method implemented imports 18kvar and consumes 1kvar more than the network with the method implemented. This means that the import of reactive power into the distribution network is subject to losses within the distribution network. The reactive power losses in the distribution network come from inductive power system equipment which absorbs reactive while operating. Transformers can be characterized as inductive equipment by their leakage reactance. The transmission lines in the system are cables with large shunt admittance which means they are capacitive and thereby generate reactive power. The cables are modelled with a series reactance which counteracts the generation of reactive power when operated under high loads as reactive power losses in the inductive reactance increases with the square of the current. By investigating further into the reactive power losses shown in Figure 3.4, it appears that the losses in the system changes throughout the day. In hour 4, the reactive power losses are 254kvar for the network with the congestion management method and 271kvar for the network without the method. In the majority of the day, the imported reactive power is higher than the consumed reactive power. However, in hour 8, more reactive power is consumed in the network than being imported in both scenarios. As the loading in the network is very low in hour 8, the inductive losses from network equipment are very low. This means that the generated reactive power in the cables exceeds the inductive losses in the network and some of the inductive loads connected to the 0.4kV side of the transformers receive reactive power generated in the cables. To see the effect of taking reactive power flows into account when evaluating the day-ahead dynamic tariff method, the line loading levels are investigated next. In Figure 3.5, the hourly current loading of each line in feeder 2 is shown for the scenario where the day-ahead dynamic tariff method is implemented and the scenario without the method, together with the rated current of each line, which is the dotted horizontal black line in Figure 3.5. From Figure 3.5 it is clear that when the day-ahead dynamic tariff method is implemented, the maximum current in the lines of feeder 2 is lower than without the method implemented. In hour 4 without the method implemented, the current is 11.6A higher than in the same hour with the method implemented. However, even with the day-ahead dynamic tariff method activated to alleviate congestion, the rated 48

49 IDE4L Deliverable 6.3 current of line L5 is exceeded in hours 2 and 4. Even though the method tries to alleviate congestion by respecting DCOPF power system limitations of active power transfer in transmission lines, the method cannot alleviate the congestion due to the thermal limits because it does not take active and reactive power losses and reactive power flows into account. Figure 3.5. Line current of all transmission lines in the feeder 2 with a penetration level of 25% DERs compared to their respective current limits shown as black vertical lines with different line styles. The left plot shows the simulation results with the day-ahead dynamic tariff method activated, the right plot shows simulation results without the method activated To see how the penetration level of 25% DERs in the distribution network affects the voltage stability in the network, the voltage level at the 10kV buses within the network is investigated next. Each bus is connected to a cable and in each end of the cable, represented by a pi equivalent circuit, there is a shunt capacitance. This shunt capacitance increases the voltage at the buses. The pi equivalent circuit also contains a resistance and an inductive reactance which combined with the current cause a voltage drop across the line. If the cable had no additional connections, the shunt capacitances would exceed voltage drop across the resistance and inductance and cause higher voltages at the connected buses. However, an inductive transformer connected to an inductive load is located at each 10kV bus in the distribution network. The inductive nature of the transformer and the load causes an overall voltage drop at each bus in the network, which means the voltage magnitude decreases further away from the external grid in the distribution network. As the bus voltage magnitude decreases further away from the external grid connection, reactive power flows towards the end of the feeders. In power system theory, there exists a strong relation between reactive power flow and voltage magnitude of buses. The reactive power will flow from a bus in the 49

50 IDE4L Deliverable 6.3 network with high voltage magnitude to buses with low voltage magnitude. For each of the two scenarios simulated, the 10kV bus with the lowest voltage magnitude for each feeder is shown in Figure 3.6. Figure 3.6. Voltage magnitude at 10kV buses furthest away from the external grid with a penetration level of 25% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder. In Figure 3.6Figure 3.6 the interesting feeder is feeder 2, where the DTs are larger than zero for a penetration level of 25% DERs in the distribution network. For this feeder, bus B6 is the bus with the lowest voltage magnitude. In hour 4, where the DT method alleviates congestion when activated, the voltage magnitude is 9.812kV. With the congestion management method deactivated, the voltage magnitude for bus B6 in hour 4 is equal to 9.807kV. This means that as the day-ahead dynamic tariff method tries to alleviate congestion and lowers the loading of the network, the voltage magnitude is increased. From Figure 3.6, the voltage magnitude of bus B6 is lowest in hour 2 as the network consumes and imports the most reactive power in this hour as seen in Figure 3.4. The reason why the scenario with the method implemented has a lower voltage in the end of the day is due to a higher consumption of active and reactive power in the HPs in this scenario. In the scenario without the method activated, the HPs are allowed to store more energy in the inside air and the house structure which means they consumes less power at the end of the day. In Figure 3.6, feeder 1 has the lowest voltage magnitude in the whole network. In hour 4, the voltage drop across power system equipment causes the voltage magnitude at bus B4 to be 9.772kV. Even though this value is lower than 10kV, the voltage stability limit of ±10% is satisfied as the voltage magnitude is larger than 9kV. The bus voltage magnitudes of the 0.4kV buses are investigated next with respect to voltage stability. 50

51 IDE4L Deliverable 6.3 The voltage magnitude of the 0.4kV buses depends on the corresponding 10kV bus voltage magnitude, the leakage reactance of the connected transformer, the power factor of the connected load and the total apparent power consumption of the loads. The corresponding 10kV bus determines the sending end voltage across the 10/0.4kV transformer. A lower 10kV bus voltage magnitude causes a lower receiving end voltage magnitude. The leakage reactance of the connected transformer determines the voltage drop across the transformer in terms of absorbed reactive power. The power factor of the load determines the relationship between active and reactive power required by the load and the apparent power consumption determines the magnitude of both active and reactive power consumption. The bus with the lowest voltage magnitude for each feeder is found by analysing the results real time digital testing platform simulation. In Figure 3.7, each feeder is represented by the bus with the lowest voltage magnitude and the results from the two scenarios are compared. Figure 3.7. Voltage magnitude at the 0.4kV buses with the lowest voltage magnitude in the test case with a penetration level of 25% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder From Figure 3.7, all 0.4kV buses in the distribution network are seen operating at a lower voltage level than rated. As long as the voltage magnitude is higher than 0.36kV and lower than 0.44kV, the voltage stability limits are satisfied, which is the case for all the 0.4kV buses as shown in Figure 3.7. In Figure 3.7 a comparison between the two scenarios with and without the day-ahead dynamic tariff method activated can be made to determine its effect on the voltage magnitude of LV buses. For feeder 2 in hour 4 where the day-ahead dynamic tariff method alleviates congestion, the voltage magnitude of bus B5 is 0.2 V higher 51

52 IDE4L Deliverable 6.3 than without the method. Therefore the effects on the LV buses voltage magnitude are very small as the voltage only changes marginally. For a penetration level of 25% DERs in the distribution network, LV bus B5 has the lowest voltage magnitude in the network. The cable connecting bus B5 to the external grid is loaded at maximum capacity in hour 4, where the other buses are operated lower than the maximum level of active power transfer. This means that the LV bus B5 is highly loaded in this test case. In the following test cases, other feeders are loaded at maximum, which can decrease the voltage magnitude of their LV buses. In order to see the convergence of the DT method as a distributed control, the line loadings resulting from the planning at the DSO side and the aggregator side are tabularized in Table 3-1. It can be seen that the difference of the line loadings is very small. The maximum difference is 0.16% for line L24. The last two columns describe the line loadings from the real-time simulation results and show the difference of using the DT method and not using it. It can be seen that the peak at line L5 has been reduced from % to %. Although there is no congestion at the planning stage as shown in the third and fourth columns, there is 9.40% overloading when using the DT method. The reason is that only the active power is considered at the planning stage. Because of the reactive power consumption by the loads and the transformers, the apparent power is about 10% higher than the active power, implying a power factor about 0.9. It is suggested that the DSO put the limits lower (about 10% depending on the estimated power factor) than the real rating when calculating the DTs through the DCOPF method. Table 3-1: Loading percentage of lines during the day of operation in the distribution network with 25% DER penetration at hour 4 (highest loading) Lines Rating [kw] Planning at DSO side (with DT) [%] Planning at Agg. Side (with DT) [%] Real-time (with DT) [%] Real-time (without DT) [%] L L L L L L L21/L L L L L Test case 2 In this case study, the line current loading, bus voltage magnitudes and transformer loading are investigated in the distribution network with an increased penetration of DERs from 25% to 50%. The DTs of both feeder 1 and 2 are seen higher than zero for 1 and 7 hours respectively. From the two scenarios with and without the day-ahead dynamic tariff method activated, the imported and consumed active and reactive power is shown in Figure

53 IDE4L Deliverable 6.3 By comparing the top plots of Figure 3.8, the scenario without congestion management imports and consumes 1,813.0kW and 1,777.0kW more in hour 4 than with the day-ahead dynamic tariff method activated. This means that the day-ahead dynamic tariff method shifts 1.777MW of active power consumption from hour 4 to alleviate congestion within this hour of operation. From the two top plots, the rise in active power losses from increasing the penetration of DERs in the network can be found. As both active and reactive power imported increases, the source current magnitude increases which causes higher losses in the network. The active power losses in hour 4 is equal to 63.0kW with the day-ahead dynamic tariff method activated and 99.0kW without the method activated. The active power losses increase by 75.49% in hour 4 for the scenario with the method active and % for the scenario without the method. From the bottom plots of Figure 3.8, the curvature of the imported and consumed reactive power in the distribution network of the two scenarios is very different in the beginning of the day. This is due to the day-ahead dynamic tariff method which shifts consumption to different hours of the day. As the active power consumption is limited due to increased DTs, the reactive power consumption is indirectly limited. By looking at the reactive power losses in hour 4, the losses increase from 254kvar to 545kvar for the scenario with the method active when the DER penetration is doubled, which is equal to a percentage increase of %. For the scenario without the method, the reactive power losses in hour 4 are equal to 983kvar which is equal to an increase of % compared to the 25% DER penetration test case. Figure 3.8. External grid active and reactive power imported to the distribution network compared to the combined active and reactive power consumption of distribution network loads with a penetration level of 50% DERs. The left hand side represents the scenario with the day-ahead dynamic tariff method activated while the right hand side shows the results without the method activated 53

54 IDE4L Deliverable 6.3 As a result of increasing the DER penetration level, the active and reactive power losses increases by a large amount when using the day-ahead dynamic tariff method for alleviating congestion. This shows the importance of including active and reactive power losses in calculating the DTs of the network buses. In the DCOPF formulated optimization, these losses are not included which can affect the efficiency of the method when implementing a large amount of DERs in the future distribution network. In order to evaluate the data transfer in the real time digital testing platform the calculated energy plans with respect to active power consumption are compared to the active power transfer limit of line L5. For line L5 the active power transfer limit is seen as 1750kW, in order to find the active power loading of this line in the real time digital testing platform, the active power consumption of buses B5 and B6 is summed in each hour and the results are shown in Figure 3.9. Figure 3.9. Active power consumption in buses B5 and B6 in feeder 2 with a penetration level of 50% DERs compared to the active power transfer level used in the day-ahead dynamic tariff method From Figure 3.9, the active power consumption at loads in feeder 2 exceeds the transfer limit of 1750kW for line L5 in hours 1 to 6 and in hour 24. If the data transfer in the real time digital testing platform worked without any errors, the combined active power consumption should be lower than or equal to the transfer limit as was the result in the offline simulations. In the offline simulations, the calculated DTs in the DSO optimization are transferred directly to the aggregators using GAMS only. In the real time digital testing platform, the DTs are calculated in the DSO GAMS script, and then transferred to the DSO Matlab session through GDX files. Afterwards the DTs are transferred from the DSO to the aggregators using the OPC server. As data are transferred multiple times rounding errors can occur. Even though the rounding error is very small, the DTs are calculated in the DSO optimization with a very high precision, and even a slight rounding error can change the aggregator optimization which results in an allowed active power consumption higher than the rated active power transfer of the lines in the distribution network. 54

55 IDE4L Deliverable 6.3 As the penetration of DERs is increased to 50% in this test case, the line loading is increased due to higher currents in the network. For this penetration level, the lines in feeder 1 and 2 are subject to non-zero DTs. By analysing the results from the real time digital testing platform, feeder 5 cables are not congested at any point during the day. The line loading and the line loading limits of the feeder 1 and 2 cables are shown in Figure 3.10, where the black horizontal lines describes the line loading limits of the lines with equal line styles in the subplots. Figure Line current of cables in the distribution network feeders 1 and 2 with a penetration level of 50% DERs compared to their respective current limits shown as black vertical lines with different line styles. The line style of each line current plot correspond to the same styled limits. The left hand side plots shows the results from the scenario with the day-ahead dynamic tariff method active, while the right hand side plots shows the results without the method From Figure 3.10 feeder 1 is subject to congestion in hour 4 for lines L1 and L2 with and without the dayahead dynamic tariff method implemented. The day-ahead dynamic tariff method decreases the congestion by 0.198kA for line L1 and 0.175kA for line L2 compared to the scenario without the method. As shown in the top left plot of Figure 3.10, lines L1 and L2 are still congested when the day-ahead dynamic tariff method is active due to the formulation of the DCOPF optimization model. From the bottom right plot of Figure 3.10, the increase in DER penetration causes the cable L5 to become heavily congested in the scenario without the method. In fact, the line is operated with a current % higher than the current limit of the transmission line in hour 4. In the left bottom plot of Figure 3.10, the day-ahead dynamic tariff method increases the DT in hour 4 for feeder 2 and alleviates kA of current away from hour 4. Even though the day-ahead dynamic tariff method increases the DTs in the network, 55

56 IDE4L Deliverable 6.3 congestion is still present and for this test case, the line L5 is operated at a higher current for 11 of the 24 hours during the day. This shows the importance of recognizing both active and reactive power losses as well as reactive power flows when calculating the DTs of the network. It is expected that when increasing the DER penetration level from 25% to 50% there will still be congestion issues due to transfer of reactive power in the distribution network. In order to further evaluate the dayahead dynamic tariff method, the effect of increasing the number of EVs and HPs on the voltage magnitudes at buses as well as transformer rating levels is investigated next. In Figure 3.11, the voltage magnitude of the 10kV buses furthest away from the external grid are shown to see whether any of the buses violate the voltage stability limits. In each of the feeder subplots, the voltage magnitude is shown for both scenarios with and without the day-ahead dynamic tariff method. Figure Voltage magnitude at 10kV buses furthest away from the external grid with a penetration level of 50% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder From Figure 3.11, the scenario without the active method has a lower voltage magnitude in the beginning of the day compared to the scenario with the method active. Even though the DTs are zero for feeder 5 in both scenarios, the higher import of active and reactive power into the network in the scenario without the method increases the loading of the external grid transformer which then lowers the 10kV bus voltage magnitude of the external grid. As the 10kV bus connected to the external grid has a lower voltage magnitude, the sending end voltage is lower which decreases the voltage magnitude at all other 10kV buses in the network. From the middle plot of Figure 3.11 the bus B6 voltage magnitude is 96V lower in hour 4 in the scenario without the method. From this it can be seen that as penetration level of DERs in the network increases, the effect of the day-ahead dynamic tariff method on the 10kV buses voltage magnitude increases. By 56

57 IDE4L Deliverable 6.3 comparing the voltage magnitude at the different feeders with and without the day-ahead dynamic tariff method, it is seen that feeder 1 has the lowest voltage magnitude in the scenario without the method activated. In hour 4, the bus voltage magnitude of bus B4 in feeder 1 is equal to 9.575kV. In the scenario with the method activated, it is also bus B4 that have the lowest voltage magnitude equal to 9.628kV. From Figure 3.11 all 10kV buses in the distribution network are operated within the voltage stability limits of ±10% deviation from rated voltage. This applies to both scenarios with and without the day-ahead dynamic tariff method. In Figure 3.12, the voltage magnitudes of 0.4kV buses with the lowest voltage magnitude for each feeder are shown. For each feeder a comparison is made between the two scenarios simulated in this test case. Figure Voltage magnitude at the 0.4kV buses with the lowest voltage magnitude in the test case with a penetration level of 50% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder From the middle plot in Figure 3.12 it is seen that the difference in 0.4kV bus voltage magnitude between the two scenarios tested becomes more substantial when increasing the DER penetration compared to the middle plot of Figure 3.7. In the left plot of Figure 3.7 the 0.4kV bus in feeder 1 with the lowest voltage magnitude was bus B1. In this case, the 0.4kV bus B3 has a lower voltage magnitude. For the scenario with the day-ahead dynamic tariff method activated, the voltage magnitude of bus B3 equal to kV in hour 4. In the scenario without the method, the LV bus B3 has the lowest voltage magnitude equal to kV in hour 2. Even though the voltage magnitude of all 0.4kV buses is lower than rated voltage, the voltage stability limit of ±10% deviation from rated voltage is still satisfied in both scenarios. 57

58 IDE4L Deliverable 6.3 Similar to the case with 25% penetration level, the line loadings resulting from the planning at the DSO side and the aggregator side are tabularized in Table 3-2. It can be seen that the difference of the line loadings is marginal. The maximum difference is 0.16% for line L1. The last two columns describe the line loadings from the real-time simulation results and show the difference of using the DT method and not using it. It can be seen that the peak at line L5 has been reduced from % to %. And the peak at L1 has been reduced from % to %. Although there is no congestion at the planning stage as shown in the third and fourth columns, there is 17.44% overloading when using the DT method. Compared to the case with 25% penetration, the line overloading becomes higher because the power factor becomes even lower due to the reactive power consumption of the HPs and the transformers. It is suggested that the reactive power consumption should be compensated through capacitor banks at the 10 kv substations such that the power factor will be improved. After the power factor is improved, the DSO can put the line loading limits a bit lower than the real rating when calculating the DTs through the DCOPF method. Table 3-2: Highest loading percentage of transformers during the day of operation for each 10/0.4kV transformer in the distribution network with 50% DER penetration at hour 4 Lines Rating [kw] Planning at DSO side (with DT) [%] Planning at Agg. Side (with DT) [%] Real-time (with DT) [%] Real-time (without DT) [%] L L L L L L L21/L L L L L Test case 3 In this test case, the implementation of DERs is increased from 50% penetration to 75% penetration. As the penetration of DERs in the network is increased, the distribution network consumption of both active and reactive power increases as shown in Figure

59 IDE4L Deliverable 6.3 Figure External grid active and reactive power applied to the distribu-tion network compared to the combined active and reactive power consumption of distribution network loads with a penetration level of 75% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown As more active and reactive power is transferred in the network by the current, even more losses are present in the network. In this test case, the highest losses are still in hour 4 for the scenario with the dayahead dynamic tariff method activated, where the active power losses are equal to 78kW and the reactive power losses are equal to 668kvar as shown in the left hand side plots of Figure For the scenario without the congestion management method activated, the active and reactive power losses are also highest in hour 4 and are equal to 200kW and 2,186kvar respectively. This shows that the reactive and reactive power losses are very high with the level of penetration and it is therefore important to recognize these when formulating the optimization model. From the active power imported and consumed in the two scenarios shown in the top plots of Figure 3.13 it is seen that with a penetration level of 75% DER, the day-ahead dynamic tariff method shifts in total 4.462MW away from hour 4 compared to the scenario without the method activated. In order to see how the increased DER penetration affects the line loading of the network cables, the loading of each cable is shown in Figure

60 IDE4L Deliverable 6.3 Figure Line current of all transmission lines in the distribution network with a penetration level of 75% DERs compared to their respective current limits shown as black vertical lines with different line styles. The line style of each line current plot correspond to the same styled limits. The top plots shows the line loading with the day-ahead dynamic tariff method activated and the bottom plots shows the results without the method 60

61 IDE4L Deliverable 6.3 From Figure 3.14 it appears that the day-ahead dynamic tariff method tries to alleviate congestion in all feeders as the maximum line loading is lower in the top plots than in the bottom plots. Again, the dayahead dynamic tariff method does not consider losses or reactive power flows in the distribution network which causes the cables to be congested. In feeder 1 with the method activated, the percentage overloading in hour 3 is equal to 20.25% for line L1. In feeder two the highest overloading is in hour 8 with a percentage overloading of 23.31% for line L5. In feeder 5, the highest overloading is in hour 4 equal to 10.06% above the rated current of cable L21/L22. As expected, the lines are still overloaded when the DER penetration level is increased. Now the voltage magnitude at all buses is investigated to see whether voltage instability happens due to the implementation of 75% DERs and the controlling of their consumption through the day-ahead dynamic tariff method. The voltage magnitude of the 10kV buses furthest away from the external grid for each feeder is shown in Figure Figure Voltage magnitude at 10kV buses furthest away from the external grid with a penetration level of 75% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder By comparing Figure 3.15 and Figure 3.11, the increased number of DERs in the distribution network causes the voltage magnitude at the 10kV buses to decrease further. However, the voltage stability limit of ±10% deviation from rated voltage is still satisfied for both scenarios, which means the power system equipment connected to these buses should function as intended. The lowest voltage magnitude for the scenario with the method activated in this test case is in hour 4 for bus B25 equal to 9.557kV, which is 4.43% lower than the rated voltage of the bus. In this test case, feeder 5 is operated at a high loading in 61

62 IDE4L Deliverable 6.3 hour 4 which causes the voltage drop across power system equipment to increase. The reason why this bus has the lowest voltage magnitude is due to the very long cables connecting bus B25 to the external grid, because long transmission lines have higher reactance and resistance than short transmission lines. For the scenario without congestion management, the lowest voltage magnitude is in hour 4 at bus B4 equal to 9.349kV. The increase in DER penetration results in a larger difference between the two scenarios tested in this case. For feeder 2, the voltage magnitude of bus B6 in the scenario with the method activated is 0.285kV higher than in the scenario without the method activated. This means that the day-ahead dynamic tariff method helps not only congestion in the network but also the voltage stability of the network. To see whether the household appliances in residents connected to the 0.4kV buses in the network can function as intended, the voltage magnitude of these buses is investigated next. The 0.4kV buses with the lowest voltage magnitude are shown in Figure 3.16 for both scenarios simulated in this test case. Figure Voltage magnitude at the 0.4kV buses with the lowest voltage mag-nitude in the test case with a penetration level of 75% DERs. Both scenarios with and without the day-ahead dynamic tariff method are shown for each feeder By comparing the voltage magnitudes of the 0.4kV buses with lowest voltage magnitude for each feeder in Figure 3.16, the scenario with the day-ahead dynamic tariff method activated shows that the lowest voltage magnitude is in hour 4 in bus B25 equal to kV. This bus is connected to the a bus with a very low bus voltage as shown in Figure 3.15, which means the bus voltage at this bus will be the lowest. The voltage magnitude of bus B25 is within the stability limits when having the day-ahead dynamic tariff method activated. By evaluating the results of the scenario without the method activated shown in Figure 3.16, it appears that bus B5 has the lowest voltage magnitude in hour 2 equal to kV. This voltage magnitude is below the allowed voltage stability limit of ±10% deviation from rated voltage and the day- 62

63 IDE4L Deliverable 6.3 ahead dynamic tariff method can be seen helping the voltage stability of the LV buses and keeping the voltage magnitude within stability limits. Similar to the two previous cases with 25% and 50% penetration level, the difference of the line loadings at the planning stage is marginal. The maximum difference is 0.48% for line L24. The last two columns describe the line loadings from the real-time simulation results and show the difference of using the DT method and not using it. It can be seen that the peak at line L5 has been reduced from % to %. And the peak at L1 has been reduced from % to %. Although there is no congestion at the planning stage as shown in the third and fourth columns, there is 14.86% overloading when using the DT method. Similar to the case with 50% penetration, it is suggested that the reactive power consumption should be compensated through capacitor banks at the 10 kv substations such that the power factor will be improved. After the power factor is improved, the DSO can put the line loading limits a bit lower than the real rating when calculating the DTs through the DCOPF method. Table 3-3: Hour with highest loading percentage of transformers during the day of operation for each 10/0.4kV transformer in the distribution network with 75% DER penetration Lines Rating [kw] Planning at DSO side (with DT) [%] Planning at Agg. Side (with DT) [%] Real-time (with DT) [%] Real-time (without DT) [%] L L L L L L L21/L L L L L Global interpretation of simulation results Three case studies have been carried out to test the convergence and the efficacy of the DT method in a real-time digital simulation environment. In general, the DT method has a very good convergence as a distributed control method. The efficacy of using the DT method for congestion management has also been demonstrated. In all of the three test cases, the difference of the line loadings resulting from the planning at the DSO side and the aggregator side at the planning stage is marginal. Those small differences are due to the rounding errors of different computers and the communication channels, which is reasonable and acceptable. The efficacy of the DT method for congestion management is demonstrated by comparing the line loadings resulting from the scenario with the DT method and the one without the DT method. All three cases show that the over loadings are largely reduced by using the DT method for peak hours. The higher the penetration level of the DERs is, the larger the peak loads can be reduced. 63

64 IDE4L Deliverable 6.3 In the test cases, the issue of using the DCOPF method for calculating the DTs is discovered. The DCOPF method considers only the active power and neglects the voltage drop and the reactive power consumption. The power losses have also been neglected by the DCOPF method. However, for 10 kv networks, the power losses are small because of the small resistance and reactance of the lines. The main issue relies on the overlooked reactive power consumption of the loads with low power factors, e.g. HPs, and the transformers. The overlooked reactive power consumptions can lead to higher apparent power and higher current of the cables. The suggestion is to compensate the reactive power consumption and take into account the average power factor of the overall consumption when setting the line loading limits. Although there is no voltage issue in all three cases when the DT method is employed, it is not always the case. When the network is very weak and the voltage issue is more critical than line loading issue, the DTs calculated by the DCOPF method may have problem. Therefore, for very weak networks, the ACOPF or a modified DCOPF taking voltage into account is suggested to be employed for determining DTs. 4 SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION 4.1 Emulation goals Demonstration of the performance and the added value of selected PMU applications. Calculation of KPIs Voltage stability of the electricity system and TSO s visibility of distribution network. 4.2 Setup description The PMU algorithms are evaluated through a real-time hardware-in-the-loop setup at KTH Royal Institute of Technology in Sweden. The system dynamics, needed as inputs to the algorithms, are simulated by realtime OPAL-RT simulator running the IDE4L reference distribution grid. The measured voltage/current are passed to PMUs which stream the voltage/current phasors to the PMU algorithms through a PDC server and an IEEE/IEC gateway. The PMU algorithms, running in LabVIEW environment on computers, receive the phasor values with the help of appropriate data mediators. Detailed of the implemented architecture can be found in the annexes of the document. 64

65 IDE4L Deliverable IEC gateway for transmission of PMU data (test scenarios and simulation results) In this section, the performance of the developed IEC [16] gateway for transmission of PMU data is demonstrated and analyzed. As presented in Section 3 of the deliverable 6.2 [11], this gateway is designed and implemented to (1) Receive and parse synchrophasor data streamed by the PDC using IEEE C [15] protocol, (2) Map the data to IEC data model and (3) Transmit the synchrophasor data through either Routed-Sampled Value or Routed-GOOSE services defined in the IEC standard IEEE/IEC gateway traffic generation TEST SCENARIO INFORMATION Scenario Name IEEE/IEC Gateway Traffic Generation Scenario ID Main Objective Scenario Description Routed-GOOSE and Routed-SV Traffic Generation Validation In this study, a measurement location has been specified on the grid model that is simulated by the OPAL-RT simulator. The estimated Synchrophasors are sent to the PDC which streams the data over TCP/IP to the crio holding IEEE-IEC Gateway. On the workstation, the Receiver part of library receives the real-time streams of data in IEC format and parses the R-SV or R-GOOSE messages. Connected KPIs - Connected Additional Performance of the gateway in terms of the imposed latency Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of a power system model in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasors to the IEEE/IEC Gateway 4 Validating the generated traffic by Wireshark Data PMU data frame Data ID 1 Methodology for data collection Wireshark EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection Location of Data collection KTH Wireshark SmarTS Lab GENERAL COMMENTS Frequency of data collection Minimum monitoring period 0.8 Hz 20 ms Details of the test together with performance analysis of the gateway can be found in in the annexes of the document. 65

66 IDE4L Deliverable Steady state model synthesis of active distribution networks (test scenarios and simulation results) Presented in chapter 5 of the deliverable 6.2 [11], this application performs real-time SSMS for multiple sections of unbalanced distribution networks by acquiring real-time measurements from multiple PMUs. The method considers the high penetration of DGs in active distribution networks. Although having more PMUs provides better observability and allows the SSMS application to determine more detailed models, the developed SSMS application does not demand any requirement in terms of the number of installed PMUs Test 1: Reproduction of the equivalent model parameters in real-time Scenario Name Main Objective Scenario Description TEST SCENARIO INFORMATION Reproduction of the Equivalent Model Scenario ID Parameters in Real-Time Reproduction of the parameters of a simple equivalent model in real-time using the developed application A simple equivalent model, is included in a power system model (not shown here) and is simulated for 100 s with known parameters to reproduce the values of the parameters by the SSMS application. Connected KPIs UR Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of a power system model in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the SSMS application 4 Gathering the values of the parameters of the equivalent model Data [R, X, E, δ] Data ID 1 Methodology for data collection CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection Location of Data collection KTH LabVIEW SmarTS Lab GENERAL COMMENTS Frequency of data collection Minimum monitoring period 0.5 Hz 20 ms Details of the test can be found in the annexes of the document. 66

67 IDE4L Deliverable Test 2: Incorporating the effect of mutual inductances TEST SCENARIO INFORMATION Scenario Name Identification of Mutual Inductance Scenario ID Main Objective Scenario Description How the existence of the mutual coupling between the phases will affect the estimated value of the model parameters. Mutual inductances have been added to the equivalent model and is simulated for 100 s to estimate the values of the parameters by the SSMS application and see how the mutual inductances affect the estimated value of the model parameters. Connected KPIs UR Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of a power system model in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the SSMS application 4 Gathering the values of the parameters of the equivalent model 5 Re-simulate the equivalent model with the estimated parameters Data [R, X, E, δ] Voltage and current values Data ID 1 2 Methodology for data collection CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW.m files MATLAB Location of Data collection KTH SmarTS Lab KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 20 ms After a complete simulation of the events included in this test 100s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 67

68 IDE4L Deliverable Test 3: Model synthesis of a sample active distribution network TEST SCENARIO INFORMATION Scenario Name Model Synthesis of the IDE4L Reference Grid Scenario ID Main Objective Applying the developed SSMS application on a section of the IDE4L Reference Grid. Scenario Description SSMS application is applied on the IDE4L reference grid, developed in task 6.1. Connected KPIs UR Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the SSMS application 4 Gathering the values of the parameters of the equivalent model Re-simulate the IDE4L Reference Grid with the estimated 5 parameters Data [R, X, E, δ] Voltage and current values Data ID 1 2 Methodology for data collection CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW.m files MATLAB Location of Data collection KTH SmarTS Lab KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 20 ms After a complete simulation of the events included in this test 100s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 68

69 IDE4L Deliverable Test 4: Sensitivity analysis TEST SCENARIO INFORMATION Scenario Name Sensitivity Analysis of the SSMS Application Scenario ID Main Objective Scenario Description Sensitivity analysis of the SSMS application to see how much the output of the SSMS application is affected; by changing the operating point of the simulated grid. The IDE4L reference grid has been used as the benchmark, in which the windfarm generation at node 854 (shown in Figure ) decreases from 1 pu to 0 pu in 10 steps of 0.1 pu. The SSMS output is used to re-simulate the grid with the estimated parameters. Connected KPIs UR Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the SSMS application 4 Gathering the values of the parameters of the equivalent model Re-simulate the IDE4L Reference Grid with the estimated 5 parameters Data [R, X, E, δ] Voltage and current values Data ID 1 2 Methodology for data collection CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW.m files MATLAB Location of Data collection KTH SmarTS Lab KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 20 ms After a complete simulation of the events included in this test 600s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 69

70 IDE4L Deliverable Voltage stability indicators (test scenarios and simulation results) Presented in chapter 7 of the deliverable 6.2 [11], this application is developed for Voltage Stability Analysis (VSA) of distribution networks. The VSA application uses data from PMUs to adopt a real-time Thevenin equivalent model for network seen from a desired load bus, and then it analyzes the PV characteristics of the model to perform VSA. In addition, since it is desirable to distinguish the effects of distribution and transmission networks on the voltage stability of the system, the VSA application develops three Thevenin equivalent models representing the entire network, distribution, and transmission networks, separately. In the next step, three PV-curves each based on one of the developed Thevenin models are derived. Finally, instability indices indicating how close the operating point is to the stability limit are introduced. Note that in the following subsections, the notion of distribution network effect implies the impact of the MV section of the distribution network on the voltage stability of the bus of interest Test 1: No DG connected to the distribution network TEST SCENARIO INFORMATION Scenario Name VS Analysis when no DG is connected Scenario ID Voltage stability analysis of the distribution network when no DG is connected to Main Objective the distribution network. The IDE4L reference grid is simulated in real-time. In this case, neither the MV Scenario Description nor the LV section contains distributed generations and all loads connected to the distribution network are fed from the transmission network. Connected KPIs I TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the VS application 4 Obtaining the PV curves and voltage instability indicators EMULATION DATA COLLECTION Data P-V curves Voltage instability indicators Data ID 1 2 Methodology for data collection LabVIEW charts and CSV file LabVIEW charts and CSV file Source/Tools /Instruments for Data collection LabVIEW LabVIEW Location of Data collection KTH SmarTS Lab KTH SmarTS Lab GENERAL COMMENTS Frequency of data collection Minimum monitoring period 0.5 Hz 2 s 0.5 Hz 2 s Details of the test can be found in the annexes of the document. 70

71 IDE4L Deliverable Test 2: DG connected to the MV section of the distribution network Scenario Name Main Objective Scenario Description TEST SCENARIO INFORMATION VS Analysis when DGs are connected to the MV Scenario ID feeder Voltage stability analysis of the distribution network when two wind turbines are connected to the MV section of the distribution network. The IDE4L reference grid is simulated in real-time. In this test a wind turbine unit producing 1 MW and 0,5 MVAR is connected to the MV grid. All other conditions are the same as those in Test 1. Connected KPIs I Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the VS application 4 Obtaining the PV curves and voltage instability indicators Data P-V curves Voltage instability indicators Data ID 1 2 Methodology for data collection LabVIEW charts and CSV file LabVIEW charts and CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW LabVIEW Location of Data collection KTH SmarTS Lab KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 2 s 0.5 Hz 2 s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 71

72 IDE4L Deliverable Test 3: Loss of generation in the transmission network Scenario Name Main Objective Scenario Description TEST SCENARIO INFORMATION VS Analysis when no DG is connected to the Scenario ID transmission network Voltage stability analysis of the distribution network when the wind turbines located in the transmission network is disconnected. The IDE4L reference grid as shown in figure is simulated in real-time. In this test, all conditions are the same as those of Test 2, however the wind farm that was connected to the transmission network is disconnected. Connected KPIs I Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs 3 Streaming the phasor to the VS application 4 Obtaining the PV curves and voltage instability indicators Data P-V curves Voltage instability indicators Data ID 1 2 Methodology for data collection LabVIEW charts and CSV file LabVIEW charts and CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW LabVIEW Location of Data collection KTH SmarTS Lab KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 2 s 0.5 Hz 2 s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 72

73 IDE4L Deliverable Small-signal dynamic model synthesis and stability indicators (test scenarios and simulation results) Presented in chapter 6 of the deliverable 6.2 [11], this application is a real-time mode estimator that is capable of harnessing PMU measurements in time series to give mode estimates and characteristic system matrices in real-time. The approach is not limited by system size or its dynamic behavior but depends highly on the availability of high quality time-domain signals in real-time. This section tests a decentralized architecture for mode estimation where the modal characteristics are estimated at a local level instead of centralized control centers. For utilization purpose, the mode-estimator application has been designed to operate in both centralized and decentralized architectures Test 1: Mode estimation using centralized architecture TEST SCENARIO INFORMATION Scenario Name Centralized mode estimation Scenario ID To show the shortcoming of this architecture in identifying local forced Main Objective oscillation. The IDE4L reference grid is simulated in real-time. Four PMUs are placed in various parts of the network, ranging from HV to LV parts. There is one interarea modes inter-area oscillation in the grid of frequency 0.42 Hz. One forced Scenario Description oscillation in the load power variation is imposed at the load at point 701 in LV side. The application is run in centralized mode to estimate the modes. Connected KPIs - Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs Streaming the positive sequence phasor to the mode estimation 3 application running in centralized architecture 4 Obtaining the modes estimates from the application EMULATION DATA COLLECTION Data Mode Estimates Data ID 1 Methodology for data collection LabVIEW graphs and CSV file Source/Tools /Instruments for Data collection LabVIEW Location of Data collection KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 2 s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 73

74 IDE4L Deliverable Test 2: Mode estimation using decentralized architecture TEST SCENARIO INFORMATION Scenario Name Decentralized mode estimation Scenario ID Main Objective Scenario Description To show the advantage of this architecture in identifying local forced oscillation. The IDE4L reference grid is simulated in real-time. Four PMUs are placed in various parts of the network, ranging from HV to LV parts. There is one interarea modes inter-area oscillation in the grid of frequency 0.42 Hz. One forced oscillation in the load power variation is imposed at the load at point 701 in LV side. The application is run in decentralized mode to estimate the modes. Connected KPIs - Connected Additional Findings TEST CASE EXECUTION METHODOLOGY Emulation steps Step 1 Real-time simulation of the IDE4L Reference Grid in OPAL-RT 2 Feeding the analogue outputs of the simulator to PMUs Streaming the positive sequence phasor to the mode estimation 3 application running in decentralized architecture. 4 Obtaining the modes estimates from the application Data Mode Estimates Data ID 1 Methodology for data collection LabVIEW graphs and CSV file EMULATION DATA COLLECTION Source/Tools /Instruments for Data collection LabVIEW Location of Data collection KTH SmarTS Lab Frequency of data collection Minimum monitoring period 0.5 Hz 2 s GENERAL COMMENTS Details of the test can be found in the annexes of the document. 4.7 Calculation of KPIs This section includes the computation of two KPIs that have been defined in Deliverable 7.1 in conjunction with use case Dynamic Monitoring for TSO [13]. 74

75 IDE4L Deliverable KPI 1 (KPI ID: I) This KPI, named as Voltage stability of the electricity system, measures improvement achieved in IDE4L project in TSO s awareness of voltage instabilities occurring in its downstream distribution systems. Details of this KPI can be found in Deliverable 7.1 [13]. The KPI is obtained through the following formula: where I rt is the voltage instability index calculated based on real-time measurements and equivalent models of the distribution system at the bus of interest in the distribution system (as demonstrated in Section 4.5), and I off is the voltage instability index calculated based on distribution system conventional data (nominal P and Q) at TSO at the PCC between the transmission and distribution systems. Calculation of I off Same methodology as described in section of the deliverable 6.2 for voltage stability indicator application has been used to obtain the P-V curve at the PCC between the transmission and distribution networks of the reference grid, shown in Figure 4.1. This is done based on the nominal P and Q of the distribution system, available at TSO. Figure 4.1 shows the obtained P-V curve and the voltage instability index which is, in this case, equal to Note that this P-V curve is based on the nominal P and Q of the distribution system which means that presence of distributed generation is ignored. Therefore, when it comes to comparison of I off and I rt, the I rt should also be computed under the same condition, i.e. no distributed generation is connected. 1.4 Power-Voltage Curve Voltage Instability Index Voltage Amplitude (pu) Operational Point Active Power (pu) 0 0 Figure 4.1 Off-line computation of P-V curve at PCC between transmission and distribution networks of the reference grid. Calculation of I rt As mentioned before, I rt should be computed under the same conditions as I off is computed. This is already done in Section (Test 1 of the Voltage Stability Indicators). The results are shown in the annexes of the document, indicating that the voltage instability index at the bus of interest (which is in this case the PCC between the MV and LV networks where PMU2 sits) is Calculation of KPI I 75

76 IDE4L Deliverable 6.3 Using the computed I rt and I off and the above-mentioned formula, the KPI can be calculated for the IDE4L reference grid at the specific bus of interest mentioned in Test 1. The computed KPI shows that the application developed in IDE4L can improve TSO s awareness of voltage instabilities occurring in its downstream distribution systems for about 25% KPI 2(KPI ID: UR) This KPI, named as TSO s visibility of distribution network, measures improvement achieved in IDE4L project in models that a TSO has for its downstream distribution grids. Details of this KPI can be found in Deliverable 7.1 [13]. The KPI is obtained through the following formula: where UR rt is updating rate of the steady-state synthesis for distribution systems calculated based on realtime model reduction techniques, and UR baseline is the updating rate of the conventional models of the distribution system at TSO. The UR rt achieved by the Steady State Model Synthesis (demonstrated in Section 4.4) is 1 data frame per 2 seconds. For UR baseline, connections have been made with Statnett, the Norwegian TSO, and Energinet.dk, the Danish TSO, as pioneers TSOs in utilizing advanced technologies. According to what their representatives declare, they do not maintain any model of the distribution network. Assuming that UR baseline = 0, the KPI will be computed as: 4.8 Global interpretation of simulation results As shown by the simulation results and the computed KPIs, exchanging key information, obtained from the PMU measurements, can help tighter integration of the operation of transmission grids (HV) with distribution grids (MV and LV). In addition, transmitting Synchrophasors via IEC helps PMUs to be incorporated into the modern IEC61850-based substation automation systems. As PMUs are being deployed in different EU countries using the current IEEE standard, if is not adopted quickly enough, there will be unnecessary and additional integration work to be able to exploit PMU and IED data within software and hardware applications. 76

77 IDE4L Deliverable CONCLUSIONS Deliverable D6.3 is devoted to the design, implementation and testing of technical and economic management algorithms applied in active distribution networks. All algorithms and networks used in this work have in common their adequacy to the IDE4L architecture and similar experimental methodologies and setups have been applied. However, testing goals cover different aspects of distribution network operation and are complementary to each other. Furthermore, network models and test scenarios are different given the specific needs of each test. As a consequence of that, this chapter is organized according to the three chapters describing each group of tests, i.e. chapters 2, 3 and 4. However, it should be noted the added value of experimental testing methodologies applied in all cases, which make researchers and engineers be aware of near real-life issues that otherwise cannot be detected only with pure simulation. As an illustration of that, some drawbacks of DT algorithms previously implemented in simulations have arisen in tests and further improvements have been suggested. As a general rule, algorithms have been generically designed, but their implementation has been specified for the different scenarios and experimental setups. It should be noted that IDE4L architecture is wide enough to allow for different strategies but their effectiveness has only been demonstrated in scenarios specifically developed. Globally, the analysis of quantitative results is limited to such scenarios, although they represent reasonable futuristic network operating conditions. The objectives of task 6.4 have been satisfactorily achieved. As a general conclusion, we could say that the feasibility and technical viability of the management and control techniques of the aggregator emulated during this work have been demonstrated. 5.1 Aggregator emulation in real-time On one hand, the work presented here addresses aspects regarding the feasibility and the technical viability of real-time function of Commercial Aggregator Architecture defined in Deliverable 6.1, and, on the other hand, addresses power quality issues resulting from integration of DERs/microgrids in power systems utilizing that architecture. The generality of results is limited to a specific type of flexibility product or service (CRP product), as well as to specific scenarios and distribution network characteristics that could represent only part of the different types of present and future scenarios in Europe UNARETI MV and LV networks. Moreover, this study has specifically focused on technical aspects regarding the real-time function of the CA Architecture, whereas other Deliverable 6.1 has put the effort in addressing economic and regulatory aspects of the CA. Nonetheless, methodologies presented here are valuable from different points of view: Design and implementation of a RT CA function that allocates flexibility among DERs in CA s portfolio: conceptually, such function complies with IDE4L Architecture, specifically regarding its real-time congestion management scheme. Development of a laboratory demo useful to test the execution of the aforementioned function interacting with a microgrid Energy Management System. During such development algorithm 77

78 IDE4L Deliverable 6.3 developers had to agree on information and communication characteristics of the CA-DER interaction, thus sharing their insights for a proper solution. Development of two simple but comprehensive methodologies to generate reasonable future scenarios originally based on real-world data. Futuristic scenarios are highly subject to uncertainty and opposite winter/summer worst-case scenarios have been proposed instead. In fact, authors believe that the development of these two extreme scenarios is aligned with the fact that CRP activations should rarely occur in the system since foreseeable occurrences are anticipated and corrected based on historic knowledge and longer term forecasting in the IDE4L Architecture. The main goal and challenge addressed is summarized in the sentence how does a Commercial Aggregator manage to allocate flexibility among DERs?. In real life, DERs may fail in the delivery of flexibility to the power system, thus some of them will under/over-perform. Then another question arises, in case of under/over-performance of the aggregated flexibility, how can a Commercial Aggregator better adjust the energy delivered during the actual activation time slot?. Results obtained are summarized as follows: Two different sources of demand side flexibility have been considered: ESSs (home batteries) and HVAC (heating in the winter scenario and cooling in the summer scenario). Results suggest that ESSs in domestic environments have higher flexibility potential in terms of average power injected/substracted to/from the grid. Advanced prosumers managing EMSs typically will use ESSs in a coordinated way with other devices at their premises in order to optimize local operating costs. Interaction of EMSs with CAs adds complexity to the management of ESSs. Tests have revealed that DERs may fail in delivering the activated volume in cases where ESSs are used to comply with local optimization programs restrictions, specifically when there have been errors in forecasted power profiles. ESSs have been also proposed as a means to accommodate additional PV power into the system and avoiding related congestion at the same time. Again, the same comment of the previous bullet also applies in this case. The efficiency of flexibility delivery of pass-through CA methodologies can be improved including real-time measurements as feedback for the RT CA. From a DSO s point of view, congestions consisting of branch thermal overloadings have been eliminated via CRP activation. Positive side-effects of power curtailment are also voltage level improvements in locations close to the nodes where power is curtailed. DSO s NR and MA tools need be coordinated since branch overloadings are affected by changes in network configuration as well as changes in power injections/withdrawals. And last, but not least, with the participation of CAs, congestion at MV level is also eliminated through power modulated at LV level. Active power losses within the corresponding LV network where power is modulated further increase the amount effectively curtailed at the MV node of the MV/LV substation since losses might get also curtailed. 5.2 Dynamic tariff The convergence and efficacy of the dynamic tariff method in real time digital simulation has been shown. Three case studies have been carried out in order to perform the tests and, in general, the DT method has a very good convergence as a distributed control method. The efficacy of using the DT method for congestion management has also been demonstrated by comparing the line loadings resulting from the scenario with 78

79 IDE4L Deliverable 6.3 the DT method and the one without the DT method. All three cases show that the over loadings are largely reduced by using the DT method for peak hours. The higher the penetration level of the DERs is, the larger the peak loads can be reduced. In the test cases, the issue of using the DCOPF method for calculating the DTs is discovered. The DCOPF method considers only the active power and neglects the voltage drop and the reactive power consumption. The power losses have also been neglected by the DCOPF method. However, for 10 kv networks, the power losses are small because of the small resistance and reactance of the lines. The main issue relies on the overlooked reactive power consumption of the loads with low power factors, e.g. HPs, and the transformers. The overlooked reactive power consumptions can lead to higher apparent power and higher current of the cables. The suggestion is to compensate the reactive power consumption and take into account the average power factor of the overall consumption when setting the line loading limits. Although there is no voltage issue in all three cases when the DT method is employed, it is not always the case. When the network is very weak and the voltage issue is more critical than line loading issue, the DTs calculated by the DCOPF method may have problem. Therefore, for very weak networks, the ACOPF or a modified DCOPF taking voltage into account is suggested to be employed for determining DTs. 5.3 Synchrophasor applications for enhancing TSO-DSO interaction Section 4 has shown by means of simulation results that exchanging key information can help tighter the integration of the transmission grids with the distribution grids. As shown by the simulation results and the computed KPIs, exchanging key information, obtained from the PMU measurements, helps the integration of the operation of transmission grids (HV) with distribution grids (MV and LV). In addition, transmitting Synchrophasors via IEC helps PMUs to be incorporated into the modern IEC61850-based substation automation systems. As PMUs are being deployed in different EU countries using the current IEEE standard, if is not adopted quickly enough, there will be unnecessary and additional integration work to be able to exploit PMU and IED data within software and hardware applications. 79

80 IDE4L Deliverable 6.3 REFERENCES [1] Catalonia Institute for Energy Research (IREC), Dansk Energi (DE), Denmark Technical University (DTU), Universidad Carlos III de Madrid (UC3M), RWTH Aachen University, A2A Reti Electtriche Spa, Unión Fenosa Distribución, S.A. (UFD), Østkraft Holding A/S, IDE4L D6.1 Optimal scheduling tools for day-ahead operation and intraday adjustment. Part II: Commercial Aggregator concept, [2] Catalonia Institute for Energy Research (IREC), IDE4L D6.2 Optimal scheduling tools for day-ahead operation and intraday adjustment. Part III: Commercial Aggregator tools and test results, [3] European Commission, A policy framework for climate and energy in the period from 2020 to 2030., [4] European Commission, Delivering the internal electricity market and making the most of public intervention., [5] Smart Grid Task Force. Expert Group 3., Regulatory Recommendations for the Deployment of Flexibility., [6] EURELECTRIC, Flexibility and Aggregation. Requirements for their interaction in the market., [7] ADDRESS Active Distribution network with full integration of Demand and distributed energy RESourceS. [Online]. Available: [8] Fernando Salazar (UFD), Fernando Martín (UFD), Maite Hormigo (GNF), IDE4L D7.1 KPI Definition, [9] Smart Energy Demand Coalition (SEDC)., Mapping Demand Response in Europe Today 2015., [10] Dansk Energi (DE), Universidad Carlos III de Madrid (UC3M), Tampere University of Technology (TUT), IDE4L D5.2/3 Congestion Management in Distribution Networks, [11] Distribution network dynamics monitoring, control, and protection solutions including their interface to TSOs, deliverable 6.2 of EU FP7-IDE4L (ideal grid for all). Available: [12] Specification and implementation of reference distribution grids and microgrids, deliverable of EU FP7-IDE4L (ideal grid for all). Available: [13] KPI Definition, deliverable 7.1 of EU FP7-IDE4L (ideal grid for all). Available: 80

81 IDE4L Deliverable 6.3 [14] D. Bakken, A. Bose, C. Hauser, D. Whitehead, and G. Zweigle, Smart generation and transmission with coherent, real-time data, in Proceedings of the IEEE, vol. 99, no. 6, pp , [15] IEEE Std C37.118, IEEE standard for syncrophasor measurements for power systems no. 2, pp , [16] Communication networks and systems for power utility automation - Part 90-5: Use of IEC to transmit synchrophasor information according to IEEE C37.118, IEC TR , [17] R.S. Singh, H. Hooshyar, L. Vanfretti, "Assessment of time synchronization requirements for Phasor Measurement Units", PowerTech Conference 2015 Eindhoven, pp.1-6, June July [18] Q. Yingying, S. Di, D. Tylavsky, "Impact of assumptions on DC power flow model accuracy" North American Power Symposium (NAPS), 2012, vol., no., pp.1-6, 9-11 Sept

82 Project no: Project acronym: IDE4L Project title: IDEAL GRID FOR ALL Deliverable 6.3: Emulation of the aggregator management and its interaction with the TSO-DSO Annex A Detailed Setup Description Due date of deliverable: Actual submission date: Start date of project: Duration: 36 months Lead beneficial name: Tampere University of Technology, Finland Writers/authors: RWTH, IREC, KTH, DTU Dissemination level: Public

83 IDE4L Deliverable 6.3 Annex A Track Changes Version Date Description Revised Approved /07/2016 First complete version of the deliverable /07/2016 Comments and feedback collected and addressed /07/2016 Final revision and document finalisation Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero, Gerard del Rosario (IREC), Hossein Hooshyar (KTH) Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero (IREC) 2

84 IDE4L Deliverable 6.3 Annex A TABLE OF CONTENTS: 1 AGGREGATOR EMULATION IN REAL-TIME Introduction Local concentrators The Configurator Microgrid concentrator Test initialization procedure Energy Management System RWTH Lab Infrastructure Communications between CA and EMS Real time Commercial Aggregator The Distribution Network Simulator Point of Common Coupling Simulator DYNAMIC TARIFF (DT) Distribution Network model in RSCAD Real Time Digital Testing Platform Open Platform Communication Server Distribution System Operator Aggregators RTDS SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION KTH setup description

85 IDE4L Deliverable 6.3 Annex A 1 AGGREGATOR EMULATION IN REAL-TIME 1.1 Introduction The goal of the μgrid at the IREC Smart Lab is to emulate the operation of a group of distributed energy resources (DERs) and, as such, it includes real and emulated components, both custom-made and industrial. It is designed to be both configurable and realistic. It is composed of 2 production layers and 1 configuration layer, as shown in Figure 1.1. The upper --production-- layer is composed of the supervisory and control devices. It includes the μgrid concentrator, the EMS, and the rest of the service nodes --that add capabilities to the system. The medium --production-- layer is composed of the DER cabinets. The bottom layer enables the configuration of the DERs. Figure 1.1 IREC Energy Smart Lab layout The μgrid is designed to be both configurable and realistic. Its DERs, are 4kW back-to-back converters, linked to the regular grid on side and to the μgrid --grid--- on the other. On the μgrid side, they inject or draw active and reactive power following the same profile a solar panel, a micro wind turbine, a residential load, a battery, et cetera, would provide. This is achieved by the lower level controls running to the power converters and the local concentrators. In addition to the emulated DERs, the μgrid can use real systems, as the 10kW 2nd Life Battery Cabinet used on the use cases described for WP6. Figure 1.2 shows the specific layout of devices and services in use for this Task. Regarding the specifics of the different intelligent electronic devices (IEDs) and supervision and control services (emulators configurator, microgrid concentrator, EMS irems and the remote simulator), they are detailed in the following sections. 4

86 IDE4L Deliverable 6.3 Annex A Figure Specific layout for WP6 1.2 Local concentrators The local concentrators are embedded linux boards with custom-made software linked with the power electronics through CAN, with the μgrid concentrator through modbus TCP/IP --through a first NIC--, and with industrial meters through RS485. During the system boot, they are setup by the Configurator through modbus TCP/IP --through a second NIC. These IEDs concentrate the data generated during these communications --setpoints, states, commands and readings-- and bridges them to the proper systems. They contain power profiles from the real systems we want to emulate, and as it is custom made software, we can add any kind of local behaviour or capability, for instance, artificial disturbances or ancillary services. The local concentrators boards have the following specifications: O.S.: Ångstrom Processor: T.I. 800 MHz RAM: 512 MByte of DDR3 RAM Flash: 512 MB emmc + μsd card 2x Ethernet 10/100/1000 Mbit/s 1x HDMI 4x USB 2.0 OTG + 2x Host ports 4x RS232 5

87 1x RS485 1x CAN 1x SPI 2x I 2 C Size: 130 x 100 mm They run the following threads: IDE4L Deliverable 6.3 Annex A Main: 10Hz Power profile 1 line per second CAN Transmit for commands. CAN Receive for states and data readings. Human-Machine Interface Modbus TCP/IP server for operation Modbus TCP/IP server for configuration Modbus RTU Client for industrial meters The 2nd Life Battery Cabinet is controlled by a local controller on the following board: O.S.: Debian with kernel Processor: i.mx28x 450 MHz RAM: 256 MByte of RAM Flash: 4 GB emmc + SD card 2x Ethernet 10/100 Mbit/s 2x USB 1x RS232 1x RS485 2x CAN Size: 175 x 120 mm The 2 switches that connect the systems have 24 ports at up to 1000 Mbps. All devices use static IP and their MAC is known. Therefore, all nodes can be auto detected and their services checked and started automatically. 1.3 The Configurator The Configurator is the only service on the configuration layer. It can use a second network to keep all configuration data away from the operation network --in case the Ethernet grid will be sniffed-- or it can use the operation network --as in IDE4L. During the use case initialization procedure, after the local concentrators have booted, the Configurator assigns the DER type, power range, which one of the available profiles for that type should be used and emulated start time. It only needs to be executed once. If the use case needed it, though, it could be used as a perturbation generator. 1.4 Microgrid concentrator The μgrid concentrator is the device that communicates with all Modbus TCP/IP nodes and centralizes all the information. It is implemented on the following machine: O.S.: Ubuntu LTS Processor: 3.6GHz 6

88 IDE4L Deliverable 6.3 Annex A RAM: 16GB HD: 2TB 2 wired network interfaces It communicates with IREC s Energy Management System IREMS using XML through sockets. For IDE4L, a complete cycle of updates is finished each second. The EMS provides an optimal supply/demand planning of energy and an energy balance in real time. It needs a number of inputs to run its algorithms: Weather forecast affecting renewable energy output. Energy prices forecast or demand forecast to plan the correct balance of the system. It runs on the following machine: O.S.: Windows 7 SP1 32-bit Processor: 2.4GHz RAM: 3GB 1 wired network interface The programming of both the μgrid concentrator and EMS is custom-made. 1.5 Test initialization procedure In order to run a use case scenario, we need to perform the following tasks on the μgrid: 1. Power the DER cabinets and the 2nd Life Battery Cabinet. 2. Power the Local Concentrators, the μgrid Concentrator, the EMS and the additional services we might need. 3. Run the initialization script on the Local Concentrators and execute the application. 4. Command the DERs to pre-charge and then to switch. 5. Log into the 2nd Life Battery Concentrator, and execute the application. 6. Start the 2nd Life Battery Cabinet. 7. Run the Configurator to set type, profile, power range and time of start. 8. Set the Local Concentrators on automatic set point transmission. 9. Start the μgrid concentrator. 10. Start the EMS. 1.6 Energy Management System IREMS is an Energy Management System for microgrids based on an optimal supply/demand planning of energy and with an energy balance in real time. It needs a set of inputs to run its algorithms: weather forecast affecting renewable energy generation, energy prices forecast or demand forecast to plan the correct balance of the system. 7

89 IDE4L Deliverable 6.3 Annex A Figure 1.3: IREMS architecture The tool consists in three modules: Energy Planner (Optimization Module) optimizes the power to be consumed, generated or stored by every node in the microgrid within a 24-hour scope. Figure 1.4: Optimal energy schedule for 24h Energy Balance (Real Time Module) calculates the power that have to be consumed or generated by every node in the microgrid based on measurements and following the optimal plan calculated 8

90 IDE4L Deliverable 6.3 Annex A by the Energy Planner. This module is responsible of sending commands to the controllable nodes of the microgrid (within a 2 or 5-second scope). Demand Forecaster is a module of IREMS which learns from the collected data in order to predict demand profiles. This module is based on machine learning algorithms. IREMS is able to work as a tool for a Commercial Aggregator (CA). The CA gathers flexibility products from its consumer portfolio and it optimizes its trading in electricity markets aiming to maximize its profits. 1.7 RWTH Lab Infrastructure The core competency of the Institute for Automation of Complex Power Systems (ACS) through its lab infrastructure, composed of appropriate hardware and software components, is to facilitate real time simulation of multi physical systems and provide a platform for prototyping and Hardware In the Loop (HIL) and Power Hardware In the Loop (PHIL) testing of industrial products and applications. For real time power system simulation, the lab is equipped with Real Time Digital Simulator (RTDS). The installed RTDS is composed of 8 racks with a total of 32 processor cards that can accurately and reliably simulate dynamics of power systems generally in the range of 50µs which can also be brought down to 2µs in some special cases. Through its various output boards real time measurement data is made available in analog and digital formats for external devices. It also can send measurement data using the network protocols like the Generic Object Oriented Substation Event (GOOSE), IEC (Sampled Values), Distributed Network Protocol (DNP3) and Synchrophasors via Ethernet. To enable the simulation of multi-energy systems, like whole city districts with thermal and electrical behaviours, PC cluster infrastructure of the lab is utilised. An in-house developed Co-Simulation platform is used to simulate the different simulation scenarios. The Lab is equipped with a Digital Signal Processing cluster (DSP) which along with RTDS is used for a multiphysics, power hardware in the loop test bed with power emulation of both the mechanical and electrical boundary conditions at terminals of the nacelle (a wind turbine without rotor and tower). The nacelle is installed onto the test bench and operated by its original controller. For the PHIL the lab is equipped with Flexible Power Simulator (FLePS) flexible electrical port to HIL and especially PHIL testing. This interface can be used in the laboratories to test small apparatuses (less than 20 kw). The lab is also equipped with Network emulator based on standard PC architecture and open-source Wide Area Network Emulator (WANem) software for emulating communication network characteristics. This enables a joint simulation of power system with communication infrastructure in order to analyze the interactions and interdependence between the two systems. For advance monitoring application development, the lab has acquired 4 commercial Phasor Measurement Units (PMUs) from Alstom and built a prototype PMU based on NI CompactRIO (NIcRIO). The ALSTOM PMUs are equipped to receive one 3-phase voltage and four 3-phase currents through its input transformers board and generate synchrophasors based on the IEEE C standard. The NIcRIO PMU is able to generate dynamic phasors abiding to the IEEE C standard. Based on the open source software OpenPDC, Phasor Data Concentrator (PDC) and hierarchy of the PDCs is implemented. For implementing the SCADA Kepware and King SCADA software are used. 9

91 IDE4L Deliverable 6.3 Annex A Figure 1.5: RWTH Lab Infrastructure. Exploiting the infrastructure available at the ACS lab a real time power system monitoring platform is built that incorporates RTDS, SCADA systems and the dedicated machines running the algorithms needed for the emulation of the CA. The monitoring platform can be functionally divided into three main parts namely the Real Time Simulation of Power Systems, the communication and data exchange interfaces and the Software Platform that enables the development of monitoring and control applications. The platform emulates the measurement system of real-world power grid thus enabling the testing of advance monitoring and control applications. The scheme of the proposed monitoring platform is shown in Figure 1.6. The Real Time Digital Simulator (RTDS) performs real-time power system simulation and provides measurements via its communication card, using the DNP3 protocol. A modular software platform is set up that is responsible for collecting the measurements of different formats, coming from measurement devices or aggregators store them in a common data storage point and provide a platform for developing monitoring, control applications in a plug and play model. With such a flexible software platform emulation of an aggregator is possible. A supervisory control and data acquisition (SCADA) system, with database, is set up to manage all the data for the use by power system applications. This platform can also be connected to the communication network emulator available in the lab infrastructure, for joint simulation of power monitoring and communication systems. The control algorithms needed to emulate the behaviour of the Commercial Aggregator are run on a dedicated computer equipped with MATLAB and a specific MATLAB extension, called OPC Toolbox, needed to connect to the OPC server and establish the bidirectional communication channel needed for the data exchange between the real time simulator, the Commercial Aggregator and the Market Agent. 10

92 IDE4L Deliverable 6.3 Annex A Figure 1.6: RWTH monitoring platform. 1.8 Communications between CA and EMS 1. The Aggregator sends a message (Status) to the EMS for request of activation of this functionality. 2. The EMS responses with an acceptance (OK) or refusal (KO) and sends the necessary information. The EMS responds within a maximum period of 2 minutes from the time in which the message is receipt. 3. The CA calculates the distribution of the flexibility offer among its portfolio of clients, and sends a message (Set) with a power set-point within the available range of flexibility of the EMS. The EMS responds within a maximum period of 2 minutes from the time in which the message is receipt. 4. The EMS applies the functionality, 4 minutes after receiving the message (Status) with the required power for the next 15 minutes. 5. The CA can send messages (Get) and (Set) within the time period of 15 minutes to check or modify the behaviour of the EMS. 6. The CA can repeat the step (1) followed by steps (2) and (3). This allows to link regulated time periods of 15 minutes. The CA has to send the message (status) 4 minutes before the end of the current period. 7. If the step (6) has not done before the end of the current period, the functionality is deactivated by the EMS. In order to establish the communication between the CA and IREMS, three messages have been defined: Message: Status CA requests activation of this functionality for a determinate time period (15 minutes), which starts after a certain time (4 minutes) since from the receipt of request of the Status. Request: 11

93 IDE4L Deliverable 6.3 Annex A <Request> <Action id="scheduled_regulation_interconnection_power_status"> <Parameter id="period" value="15" /> <Parameter id="start_in" value="4" /> </Action> </Request> IREMS response: 1. NO: the functionality is not activated. i. IREMS continues with its normal performance. <Response> <Action id="scheduled_regulation_interconnection_power_status" value= KO /> </Response> 2. YES: the functionality is activated (pending receipt a power set-point). i. The EMS sends 3 power values for the required time period; baseline (P b ), potential increase ( P b ) and potential decrease ( P b ). The EMS also sends the starting time to apply the request (in UTC). <Response> <Action id="scheduled_regulation_interconnection_power_status" value= OK > <Parameter id="baseline_power" value="15" /> <Parameter id="power_up" value="4" /> <Parameter id="power_down" value="2" /> <Parameter id="start_time" value=" t15:35:27z" /> </Action> </Response> Message: Set The CA sends a power set-point (P) within the range (P b P b P P b + P b ). The EMS tries to match with the required set-point during the current time period. Before starting each period, it is necessary to send a set-point in order to active the functionality. Request: <Request> <Action id="scheduled_regulation_interconnection_power_set"> <Parameter id="power" value="17" /> </Action> </Request> EMS response: <Response> <Action id="scheduled_regulation_interconnection_power_set" value= OK /> </Response> 12

94 IDE4L Deliverable 6.3 Annex A Message: Get The CA sends a request to read the accumulated energy in the grid tie for a determinate instant of time (in UTC). Request: <Request> <Action id="get_accumulated_energy"> <Parameter id="time" value=" :38:27" /> </Action> </Request> EMS response: 1. Reading of the accumulated energy of the smart meter in an instant of time close to the requested point of time. 2. Instant of time (in UTC) wherein the accumulated energy has been measured. <Response> <Action id="get_accumulated_energy"> <Parameter id="energy" value=" " /> <Parameter id="time" value=" t15:38:26z" /> </Action> </Response> 1.9 Real time Commercial Aggregator Algorithm description The Real-Time Commercial Aggregator algorithm is implemented using MATLAB and it simulates the calculations and data exchanges carried out by the CA during its operation. This program is intended to be run on a MATLAB instance in a computer that should be linked to the emulated EMS through any connection socket that allows for TCP-IP communication, and it will be executed in parallel to the Distributed Network (DN) simulator and the Point of Common Coupling (PCC) Simulator on different MATLAB instances on the same computer. For the time management, the code uses built-in timing MATLAB functions such as tic/toc and timer. It should be noted that these routines allow only for a soft real-time, and may cause asynchrony during very long executions. Would the user need a strict real-time, he/she should implement it by other means. The following paragraphs provide of a high-level description of the algorithm. For a more schematic approach, see the graphic description of the algorithm attached in Figure 1.7. The succession of main routines of the Real-time CA (RT CA) program is detailed in that Figure. It has been assumed that each of the routines corresponds to a timestamp relative to the time elapsed from the beginning of CRP activation. Routines are forced to begin at exact H:MM:00 instants, i.e. 0:00:00, 0:02:00, 0:04:00, so on. 13

95 IDE4L Deliverable 6.3 Annex A Figure 1.7 Software routines and their interactions At the beginning of the cycle, the RT CA simulator checks whether CRP flexibility is being requested by the MA through the CRP_activation.mat Boolean variable. If CRP is not activated, it will wait for two seconds and it will check it again, repeating this cycle indefinitely. Otherwise, it will start a 15-minute CRP activation interval/block: Minute 0 The CA imports the CRP volume requested by the MA from the CRP_volume.mat MATLAB file and waits for the next routine to begin (0:02:00), process (1) in Figure 1.7. Minute 2 The program requests the emulated EMS s status and reads its response (i.e. the confirmation, the baseline power, the upwards flexibility and the downwards flexibility from the microgrid EMS). Afterwards it imports the file containing all other flexible DER s statuses (process (2) in Figure 1.7) and keeps idle until the next routine starts (0:04:00). Minute 4 The CRP volume requested from the MA is allocated among the different flexible DERs (including the emulated µgrid) proportionally to their available local flexibilities (process (3)). In case that the overall 14

96 IDE4L Deliverable 6.3 Annex A available flexibility from the DERs is not enough to satisfy the MA request, 100% of such available flexibility will be requested to all the portfolio and a warning message will be sent. Minute 6 The program sends the calculated setpoint to the emulated EMS, and stores the setpoints for the rest of the DERs into the DER_activ_flex.csv file (process (4)). Afterwards it sends a Smart Meter reading request to the EMS and proceeds to receive the reading (trough TCP-IP). It also imports the simulated DERs SM measurements (from the counter_measures.csv files, which will contain a set of predetermined time and measurement values that will be further used for calculations), process (6). Minute 8 The sending of the confirmation to the Tertiary Controller is simulated by appending the current time and an OK/KO message into the CRP_confirmation.csv file (process (5)). Minute 9 The CA reads the SM values from both sources for a second time (process (6)), and calculates the increase or decrease in the CRP volume for the remaining period in order to achieve the MA requirements (process (7)). The time elapsed between the two consecutive SM readings request is 3 minutes (from 0:06:00 to 0:09:00). It should be noted that this new flexibility volume target for the remaining activation period will be larger than the originally activated one if aggregated DERs globally under-perform, and the other way around. The idea behind is to force the total delivered volume for the actual 15-min period not to differ significantly from the activated amount. Furthermore, in this process (7) the algorithm will locally ignore those DERs that have not delivered at least 80% of the activated flexibility so far, according to this rule: ΔP Delivered 0,8 ΔP Activated By applying the previous formula, the fraction of the total amount delivered by clearly under-performing DERs is transferred to performing and over-performing DERs in order to encourage consumers/prosumers comply with the CRP product specifications. Minute 11 The algorithm repeats a procedure similar to process (3) as in minute 4, but now with the new calculated flexibility target to allocate and only considering performing and over-performing DERs as eligible flexibility providers. Minute 13 The CRP setpoints calculated in the last routine are sent to both emulated EMS and simulated DERs (process (8)). Afterwards, the algorithm waits until the 15-minute activation cycle is complete and keeps checking every two seconds whether the CRP is activated or not, as is executed prior to minute 0. Implementation 15

97 IDE4L Deliverable 6.3 Annex A The program is composed of a main MATLAB code, which contains and runs the main parts of the algorithm and calls ancillary functions for communication purposes (for tasks such as establishing TCP-IP connectivity, sending and receiving data and parsing streams). The input data that is not provided through the socket connection is retrieved from either comma separated values or native MATLAB.mat files in the harddrive. The same type of data flows and files will be followed for the outputs. The following files are used or generated by the program: Table 1-1 Summary of files used or generated by the RT CA. run_rtca.m Core Algorithm File o Connect_to_EMS.m ParseXML.m Interconnection Files flex_status.csv xmlreadstring.m counter_measures.csv CRP_activation.mat Input Data CRP_volume.mat DER_activ_flex_*.csv CRP_confirmation.csv Output Data Main algorithm ( run_rtca.m ) This is the core file of the CA Algorithm. It contains the commands for data loading and variable initialization, as well as for establishing communication with the EMS through TCP-IP protocol and writing the output data onto the corresponding files. The distribution of the flexibility volume between DERs is also encoded here. The input data that does not come from the EMS has its source on the Current Folder, which is also the destination for the outputs. The timing of the algorithm is controlled by the MATLAB built-in function timer, which does not provide perfect synchronism. Should a better performance be needed, the user can adapt this algorithm to his or her convenience and use a custom clock source. These are the files where data is imported from: flex_status.csv: It contains the baseline power, as well as the upwards and downwards flexibility volumes for each simulated flexible DER in the DN for a 15 minute period. Attributes contained: flex_res_id tstamp declared_base_pow (kw) 16

98 IDE4L Deliverable 6.3 Annex A available_up_flex (kw) available_down_flex (kw) counter_measures.csv: CRP_activation.mat: CPR_volume.mat: It stores the accumulated value of the Smart Meter for every DER at a given moment in time. Net Metering is supposed, so its value will be the accumulated difference between input and output from the interconnection to the grid. Attributes contained: flex_res_id tstamp SM_meas (kwh) This boolean variable, dependent of the MA, tells whether the CRP is activated (TRUE) or disabled (FALSE). Also dependent of the MA, it stores the volume of flexibility (kw) to be requested to the CA. The following files will contain the outputs: CRP_confirmation.csv: DER_activ_flex.csv: It contains, for each 15-minute cycle, the signal sent by the CA to the EMS confirming whether the CRP activation request is being pursued (OK/KO). It contains the following variables: tstamp confirm It stores the CRP setpoints to be sent to the simulated flexible DERs in the grid model of the DN simulator, along with its activated flexibility, so the PCC Simulator can calculate the power flows. It contains: flex_res_id tstamp CRP_setpoint (kw) active_flex (kw) duration (s) Communication with EMS ( Connect_to_EMS.m ) This MATLAB function manages all interactions with the EMS during the execution of the main algorithm. It establishes the TCP-IP communication each time a message must be sent and closes it as soon as the answer is received. It also parses the input XML streams through the ParseXML.m and xmlreadstring.m to obtain the requested data The Distribution Network Simulator Algorithm description The DN simulator is the program in charge of generating the updated grid simulation cases based on the predefined MV and LV profiles (baselines) and, if the CRP is activated, on the flexibility provided by the Aggregator s portfolio. Every minute it starts a repeating three-second cycle checking for both new network base case files and flexibility files. In case it finds a new base case for the current minute, it will store this case and apply the 17

99 IDE4L Deliverable 6.3 Annex A current flexibility if convenient. In case it finds a new flexibility file, it will apply the flexibility on the future base cases for the amount of time specified on this file. It also calculates the SM readings (energy measurements) of the simulated LV users based on their consumption for three minutes and adding a random deviation. These values are to be used by the CA simulator run_rtca.m when checking the DERs individual performances. Implementation The program is composed of a main MATLAB code which contains all the casuistries and I/O operations and an ancillary function. The input data is retrieved from comma separated value files in the hard-drive and the outputs are stored in the same way. The following files are used or generated by the program: Table 1-2 Summary of files used or generated by the DN Simulator. DN_simulator.m o apply_flexibility.m base_*.m DER_activ_flex_*.csv current_*.m counter_measures2.csv Core Algorithm File Ancillary Algorithm Files Input Data Output Data Main algorithm ( DN_simulator.m ) The main algorithm contains the commands for data loading and variable initialization, along with the decision logic and data writing. The timing of the algorithm is controlled by the MATLAB built-in function timer, which does not provide perfect synchronism among different MATLAB instances running parallel. Should a better performance be needed, the user can adapt this algorithm to his or her convenience and use a custom clock source. These are the files where the pieces of data are imported from: base_*.m: DER_activ_flex_*.csv: It contains the baseline Matpower case of the MV and LV network corresponding to a given quarter of hour. The number in * specifies the moment at which it will be read. Generated by the CA, it contains the CRP setpoints which will be used by the DN to update the LV baseline cases. It contains: flex_res_id tstamp CRP_setpoint (kw) active_flex (kw) 18

100 IDE4L Deliverable 6.3 Annex A duration (s) The following files will contain the outputs: real_flex_*.csv: current_*.mat: For debugging purposes only, this file contains the actual provided flexibility at a given time, i.e. after the random deviation is applied. flex_res_id tstamp CRP_setpoint active_flex duration It contains the updated Matpower case of the network including (i) the current baseline values along with (ii) the delivered LV DER flexibility (if activated). It will be read later by the PCC simulator Point of Common Coupling Simulator Algorithm description The purpose of the PCC algorithm is to update the active and reactive power consumed by the emulated node (microgrid s PCC) on the current case from data retrieved from the grid emulator s remote control embedded PC. At the same time, it runs power flow calculations and updates accordingly the voltage magnitude at this node. Finally, it stores the current cases. The algorithm is repeated in three-second cycles. Implementation The following files are used or generated by the program: Table 1-3 Summary of files used or generated by the PCC Simulator. PHIL.m Core Algorithm File o get_data.m o send_data.m Interconnection Files xmlreadstring.m current_*.m acquired_flex_*.csv network_status_*.m emulated_grid_tension.csv Input Data Output Data Main algorithm ( PHIL.m ) 19

101 IDE4L Deliverable 6.3 Annex A This is the algorithm in charge of updating the current network case (once the LV flexibility has been applied to the grid simulation case) with the (i) MV flexibility activated by the MA in other nodes of the network and (ii) the power measurements of the Grid Emulator. After that, it solves the power flow case in order to calculate the voltage in nodes, the line saturations and the grid losses. Once the voltages are calculated, it sends the voltage set point of the emulated node to the Grid Emulator. Finally, it stores the power flow results as an output. The algorithm operates in three-second cycles, in each of which it imports the Matpower case file corresponding to the current minute of simulation (current_*.m) along with the current activated MV flexibility file (acquired_flex_*.csv). The following inputs are used: current_*.m: acquired_flex_*.csv: Matpower case of the network including the delivered LV node flexibility generated by the DN simulator. It contains the active flexibility delivered by the MV nodes selected by the MA. Attributes contained: nodeid value [MW] The following files will contain the outputs: network_status_*.m: Grid simulation case at a given time (expressed in seconds with decimals at *), including all flexibilities (LV and MV users) and the emulated node. This is the main output of the set-up. emulated_grid_tension.csv: Values of the grid voltage level calculated by the Matpower power flow algorithm at the emulated node. It contains: seconds [s] volts [V] 2 DYNAMIC TARIFF (DT) 2.1 Distribution Network model in RSCAD The implementation of the MV distribution network of Rønne is done in this emulation by modelling transmission lines, external grid, transformers and loads. Implementing transmission lines is done through pi equivalent circuits, as transmission lines shorter than 15 km cannot be implemented as transmission line models in RSCAD.\\eonakku\home\acs\gli\Desktop\D6.3\d6.3- DT emulation.docx - page146 The pi equivalent circuit representation of transmission lines is widely used in power system studies and is valid when the system is balanced and the phase voltages and currents are shifted by ±120, which is the case in this emulation as only steady state operation of the distribution network is considered. Real transmission lines can be characterized by distributed resistances in series with inductances and in parallel with a shunt capacitance per unit length. In the pi equivalent circuit these parameters are lumped over the entire length of the transmission line which represents each transmission line by a single resistance R, a single inductance XL and a shunt capacitance XC /2 at each terminal. 20

102 IDE4L Deliverable 6.3 Annex A In order to reduce the number of electrical nodes in the distribution network, the connections between buses represented by more than 1 transmission lines is reduced through the Kron reduction method. In power system flow studies, the Kron reduction method is used to obtain a network-reduced model of a large power system. The Kron reduction method recalculates the admittance matrix of the power system by Gaussian elimination. In the Kron reduction method the buses that have zero current injection are eliminated. The current injection is zero when neither a generating nor consuming unit is located at the node. As only the transmission lines connected to buses have a current injection, the intermediate buses can be eliminated. The admittance matrix of the distribution network of Rønne are found from the transmission line data represented by a series resistance R, a series resistance XL and a shunt reactance XC. The distribution of aggregator subscribed DERs is described as follows. DERs connected to feeder 1 and 2 are assumed subscribed to aggregator 1 while DERs connected to feeder 5 are assumed subscribed to aggregator 2. Now, the distribution network is reduced to 11 buses. In Figure 2.1 a single line diagram of the distribution network implemented in RSCAD is shown. 21

103 IDE4L Deliverable 6.3 Annex A Figure 2.1: Topology of 10kV distribution network connected to the NOR bus in the city of Rønne implemented in the real time digital testing platform for online tests. By looking at Figure 2.1, it becomes apparent that the online testing representation of the distribution network can be further reduced by performing the Kron reduction method on lines L21 and L22. The Kron reduction method is performed on all lines in the reduced distribution network with zero current injection at the buses as well as the intermediate bus between L21 and L22. In Table 2-1 the lumped circuit parameters of all transmission lines in the reduced distribution network are shown. Table 2-1:. Transmission line parameters in Kron reduced distribution network Line Length R X L X C 22

104 IDE4L Deliverable 6.3 Annex A [km] [Ω] [Ω] [Ω] L ,164 L ,149 L ,695 L ,183 L ,389 L ,200 L21/L ,574 L ,686 L ,513 L ,663 L ,690.3 After the modelling of the transmission lines is done, the external grid model is investigated. In the distribution network of Rønne, the external grid is the 60kV network shown in Figure 2.1\\eonakku\home\acs\gli\Desktop\D6.3\d6.3- DT emulation.docx - page105 at bus NOR. The external grid is modelled as a three phase voltage source src in series with a small resistance and implemented in RSCAD as shown in Figure 2.2. Furthermore, the voltage source is set capable of controlling the system frequency at a constant value of 50 Hz. Figure 2.2. RSCAD implementation of the external grid connection in the NOR bus The transformer TNOR in Figure 2.2 is modelled using characteristic data consisting of apparent power rating, connection type of primary and secondary winding, rated voltage of primary and secondary transformer terminals, leakage reactance of the transformer, no-load current and no-load losses. For the TNOR transformer, the primary side is connected to the bus NORHV in Figure 2.2 while the secondary side is connected to the NORLV bus. The step down transformer TNOR has a primary side voltage of 60kV and a secondary side voltage of 10kV. The primary side of TNOR is connected in star while the secondary side is connected in delta as seen in Figure 2.2. The apparent power rating is given in MVA and characterizes how much apparent power can be handled by the transformer before it is overloaded. If a transformer is operated at a higher MVA for a longer time period, the transformer windings heat up. If the transformer windings increase too much in temperature, the conducting and insulating material starts to degrade and leads to a failure of the transformer. The leakage reactance of the transformer is characterized by the leakage flux which only links one of the windings. The leakage reactance of the transformer defines the transformer reactive power losses. 23

105 IDE4L Deliverable 6.3 Annex A The no-load current and no-load losses characterize the efficiency of the transformer. The no-load current is the current that flows in the primary winding when the secondary winding is open circuited. This current excites the transformer core and causes a flux to circulate. The no-load current is also known as the excitation current and is represented by a percentage of rated current of the transformer. The no-load losses characterize losses inside the transformer due to hysteresis and eddy current losses inside the transformer core. Besides the transformer connecting the external grid to the MV distribution network, each 10kV bus is connected to one or two transformers which transfer power to the 0.4kV buses where the loads are connected. All the transformers are implemented as fixed tap transformers and their parameters are shown in Table 2-2 for all the transformers in the reduced distribution network shown in Figure 2.1. Table 2-2. Transformer parameters in Kron reduced distribution network Primary Secondary Primary Secondary No-load No-load Transformer connection connection voltage voltage S r XT current losses TNOR star delta 60kV 10kV 10MVA p.u. 0% 0p.u. T1 delta star 10kV 0.4kV 0.63MVA p.u % p.u. T2a delta star 10kV 0.4kV 0.4MVA p.u. 0.18% p.u. T2b delta star 10kV 0.4kV 0.63MVA p.u % p.u. T3 delta star 10kV 0.4kV 0.63MVA p.u. 0.14% p.u. T4 delta star 10kV 0.4kV 0.4MVA p.u % p.u. T5a delta star 10kV 0.4kV 0.4MVA p.u % p.u. T5b delta star 10kV 0.4kV 1.25MVA p.u % p.u. T6 delta star 10kV 0.4kV 0.63MVA p.u % p.u. T21 delta star 10kV 0.4kV 0.8MVA p.u % p.u. T22 delta star 10kV 0.4kV 0.63MVA p.u % p.u. T23 delta star 10kV 0.4kV 0.4MVA p.u % p.u. T24 delta star 10kV 0.4kV 0.4MVA p.u % p.u. T25 delta star 10kV 0.4kV 0.15MVA p.u % p.u. As stated, each 0.4kV bus is connected to a load. These loads represent the houses connected to each bus and are characterized by their active and reactive power loading. To simplify the distribution network, all houses are assumed connected directly to the 0.4kV bus and no 0.4kV transmission lines are included. The reduced distribution network of Rønne is implemented in the RSCAD software as shown in Figure 2.3, where the lines are shown as L1 to L26, the transformers are shown as T 1 to T 25 and the loads are shown as D1 to D25. 24

106 IDE4L Deliverable 6.3 Annex A Figure 2.3. Grid topology of the northern Rønne distribution grid implemented in the RSCAD software as part of the real time digital testing platform The average active and reactive power consumption levels are defined for each load point. From the active and reactive power consumption, the magnitude of apparent power consumption S is calculated using, S = P 2 + Q 2. The power factor of each load point base load demand is found using the active power and apparent power. The calculated power factor for the base load demand is found for each bus as lagging and is shown in Table 2-3. Table 2-3. Base load demand power factor for all buses in the reduced distribution network used in online simulations Load Active power Reactive power Apparent power Power factor # [MW] [Mvar] [MVA] [-] D D D D D D D D

107 IDE4L Deliverable 6.3 Annex A D D D In Figure 2.3, each load component is connected to two control signals characterizing the active and reactive power, P 1 to P 25 and Q1 to Q25, respectively. These signals are used in the real time digital testing platform to describe the power consumption at each load point in each hour of operation. The active power consumption at each bus and hour is defined by the energy plans calculated by the aggregators. The reactive power consumption is calculated using the power factor of HPs and the power factor of base load demand. In the following section, the establishment of the real time digital testing platform is described in terms of communication channels. 2.2 Real Time Digital Testing Platform The real time digital testing platform created in this emulation contains a distribution network representation implemented in the RTDS as explained in the above section, an OPC server which handles the communication of DTs from DSO to aggregators and energy plans from aggregators to the RTDS. Inside the real time digital testing platform, a DSO client is implemented with a MATLAB and a GAMS script which are used to perform the day-ahead dynamic tariff method optimization. Likewise, two aggregator clients are implemented which perform the optimizations at the aggregator side for the day-ahead dynamic tariff method through a MATLAB and a GAMS script. The OPC server, DSO client and aggregator clients are implemented on different computers. The communication links inside the real time digital testing platform are shown in Figure 2.4 together with the protocols used for each communication link. Figure 2.4. Layout of the digital real time testing platform 26

108 IDE4L Deliverable 6.3 Annex A The communication links between clients in the online testing environment are established by the MatrikonOPC DNP3 server which enables a communication between an OPC protocol used by MATLAB and a Distributed Network Protocol (DNP3) used by the RTDS. In the following, the purpose and implementation of each client in Figure 2.4 are described in greater detail Open Platform Communication Server In Figure 2.4 the OPC server is placed in the middle of the digital real time testing platform. The task of the OPC server is to enable and allow communication of data between clients in the testing platform. An OPC server is a powerful tool capable of communicating across different protocols. The OPC standard on which the server is based is used in various industries to enable transfer of data between different devices and databases. As shown in Figure 2.4, the OPC server can translate the OPC protocol data from the DSO and aggregators into the DNP3 protocol and vice versa. The communication between the clients across the OPC server is enabled by utilizing the MatrikonOPC software. The software package includes the server itself and an explorer tool which can be used to monitor the server data. The MatrikonOPC software assigns data to server tags. Each server tag represents a certain data object and its value. When a device has to read or write a value to a data object, it connects to the server and specifies the required server tag in order to assure that the data is stored in the correct location. The RTDS client in this project communicates with the OPC server by receiving power consumption data assigned to control signals of the loads as shown in Figure 2.3. The communication of power consumption data requires certain server tags, these server tags are explained in depth in the following subsection. The MatrikonOPC DNP3 server is created on the computer and connected to the RTDS through the method described in. First step in the creation of the OPC communication server is to define a new server configuration. The new server is configured as a network channel named "RTDS Network Channel" using the UDH protocol. The UDH protocol is part of the internet protocol suite and allows communication to and from the OPC server through the internet. Next, a network host is created in the network channel. The network host is named "RTDS Network Host" and is connected to a RTDS rack containing a GTNETx2 card by defining the host IP address as the IP address of the GTNETx2 card. A GTNETx2 card can be implemented in a RTDS rack which enables the communication to and from the RTDS rack across the internet. The network host is further defined by a port number which specifies the internet connection between the RTDS and the OPC server. For DNP3 communication, port is registered as the common communication port. By defining the DNP3 communication port for the network host, a DNP3 unit can be defined on the network host. The DNP3 unit is named "RTDS DNP 3". By following these steps described in, a communication link across the internet is established between the OPC server and the RTDS. Besides communicating with the RTDS, the OPC server communicates with the DSO and aggregator clients. In this project, the engineering software tool MATLAB is used to establish and utilize the communication link between the DSO and aggregator clients to the OPC server. On an OPC server connected to different devices and databases, default server tags are specified by these devices, in this case the RTDS. As the DSO and aggregators in this project are controlled by the MATLAB software, artificial server tags are created which allows communication between the DSO and the aggregators; these server tags are called aliases. 27

109 IDE4L Deliverable 6.3 Annex A The desired aliases are created on the OPC server in an alias group which holds a number of aliases. The specific aliases needed for the DSO and aggregator clients are presented in following subsections. The connection of the OPC server to the DSO and aggregator clients is initialized using the MATLAB session on each client. Each client connects to the OPC server using MATLAB handles. The MATLAB session initiates the connection by defining a data access object which is defined by the OPC server host and the specific OPC server ID. The OPC server ID is specified by the network channel, network host and DNP3 unit defined in the MatrikonOPC DNP3 software. After the initial connection to the OPC server is handled, the MATLAB session defines a data access group within the data access object. Within the data access group, the MATLAB session adds data access items which correspond to server tags and aliases on the OPC server and allows reading and writing of the OPC server from the MATLAB session Distribution System Operator The DSO is implemented on the computer and represented by a MATLAB and a GAMS software session. The purpose of the DSO client in the real time digital testing platform is to calculate the DTs in the distribution network and inform the aggregators about these DTs. The DSO predicts price and base load demand data and obtains DER operational data from databases. These data are required in order to run the optimization model as explained in relative chapter. The predicted data, distribution network topology and line loading limits information for the busses and transmission lines in the reduced distribution network are defined in MATLAB and forwarded to the GAMS session in INC-files. After storing data in GAMS INC-files, the DSO solves the optimization problem using the GAMS session. After an optimal solution to the optimization problem is found, the DSO calculates the DTs in GAMS and sends the DTs to the MATLAB session through a GDX file. In MATLAB, the DSO client connects to the OPC server and stores the DTs on the OPC server using server aliases. The DSO requires server aliases to enable communication between DSO and aggregator clients. An alias is created for each of the calculated DTs for all of the busses in the reduced distribution network. These aliases are defined on the OPC server through the MatrikonOPC DNP3 software Aggregators The aggregators in the real time digital testing platform are implemented to perform as described in the main part of the deliverable. Each aggregator is implemented on the computer by a MATLAB and a GAMS software session. The purpose of the aggregators is to calculate the optimal energy plans of DERs in the reduced distribution network based on the DTs calculated by the DSO. The aggregators role in the real time digital testing platform starts after DTs are uploaded on the OPC server by the DSO client. First, the aggregators connect to the OPC server and load the DTs into the MATLAB session which stores the data in a INC-file visible for the GAMS session. Afterwards, the aggregators save predicted DER operational data inside additional INC-files. When all required data are obtained, the aggregators solve the optimization problem in the GAMS session and return the energy plan at each bus to the MATLAB session through a GDX-file. The MATLAB session then connects to server aliases on the OPC server defined for the hourly active power for each load in the network. The MATLAB session then stores the calculated energy plans for each bus on the OPC server. 28

110 IDE4L Deliverable 6.3 Annex A RTDS In the real time digital testing platform, the purpose of the RTDS is to simulate the distribution network implemented and return power system stability parameters showing the operational situation of the distribution network with the day-ahead dynamic tariff method implemented. The RTDS communicates with the OPC server through a DNP3 communication link shown in Figure 2.4. On the OPC server, a server tag is defined for each of the load point control signals shown in Figure 2.3. For each control signal, an analogue control point is defined using a points listing file, and the communication component in RSCAD shown in Figure 2.5. The control components in Figure 2.5 are used to define the GTNETx2 card that the RTDS uses to communicate with the OPC server. Furthermore, it is used to define the port number and the IP address of the OPC server. As the IP address in Figure 2.5 is set to , RSCAD allows communication to any server that establishes a communication link to the RTDS GTNETx2 card. Furthermore, the points file used to define the analogue control point in the RSCAD model is shown in Figure 2.5 as the Points File. The Assign Controls Processor in Figure 2.5 defines the location of the card on the RTDS rack connected to the GTNETx2 card. Figure 2.5. Communication control components in RSCAD to allow transfer of data from the OPC server to the RTDS In order to simulate the distribution network using the energy plans calculated by the aggregators, the simulation of the distribution network in the RTDS is started. 29

111 IDE4L Deliverable 6.3 Annex A When DSO and aggregator optimization models have been solved, a RTDS MATLAB control script connects to the OPC server and links OPC server tags for each load point s active and reactive power consumption with MATLAB data access items. After linking the OPC server tags to MATLAB data access items, the MATLAB script loads energy plans for each bus from the OPC server aliases as well as predicted base demand from DSO data and calculates the reactive power consumption for each of the load points using unity power factor for EV loads, 0.84 lagging power factor for HP consumption and the power factor for each base load demand specified in Table 2-3. When the reactive power consumption plans are calculated for each bus and hour, the MATLAB session sets the hour of operation and stores the active and reactive power of each bus in the hour of operation on the data access items linked to the analogue control points on the RTDS through the OPC server. As the RTDS is simulating the distribution network in real time using the RSCAD software, changes in the OPC server tag values happens almost instantaneously in the distribution network loads and the resulting power system stability parameters are readable from the RSCAD software. By changing the hour of operation in the MATLAB script and executing the changes, the distribution network implemented in RSCAD is simulated for this hour instead. 3 SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION 3.1 KTH setup description KTH Laboratory Infrastructure Description The SmarTS-Lab currently deployed at KTH is comprised of several components. These include hardware components and software components, which are different PDCs and application host platforms that allow users to develop applications for wide area measurement based on the data acquired from these hardware components. The current laboratory set-up consists of both software and hardware components, as shown in Figure 3.1 (1) The power system is simulated using the emegasim Real- Time Simulator from Opal-RT, capable of providing real- time analog and digital I/Os for its interfacing with hardware components. (2) The WAMPAC application host platform includes the PDC and takes the form of either proprietary software solutions from Schweitzer Engineering Laboratories (SEL). The WAMPAC application host platform interfaces with the RT simulator through the following hardware components: (3) protection relays with embedded PMU functions from Schweitzer Engineering Laboratories (SEL), (4) line differential protection relays from ABB with Optical Ether- net Module (OEM) for station and process bus implementation, current and voltage amplifiers from Megger (not shown), (5) a compact Reconfigurable Input/Output (crio) real-time controller from National Instruments, (6) a PC with a communication networks emulator (Opnet) and (7) a GPS substation clock from Arbiter Model 1094B fed by (8) a GPS antenna which provides time stamping to the PMUs and IEDs. The IED s stream data over TCP/IP using a (9) network switch, which also allows users to transfer models to the real- time targets from four independent workstations (not shown). (10)Two servers allow access to the 30

112 IDE4L Deliverable 6.3 Annex A real-time simulator from other locations within KTH. There are additional devices that provide ancillary services for the facility which are not listed here. KTH Laboratory Infrastructure Description Figure 3.2 depicts the laboratory architecture used for the performance assessment of the developed PMU algorithms. As the figure shows, the IDE4L reference grid, developed in task 6.1, is deployed and simulated in the OPAL- RT real-time simulator by utilizing its parallel distributed computing capability to comply with real-time simulation constraints. The measured voltages and currents are fed to PMUs through the analogue output ports of the OPAL-RT simulator. As indicated in the figure, the PMUs used in this architecture are SEL-421 from Schweitzer Engineering Laboratories, being fed through amplifiers. The PMU data are then sent to a PDC which streams the data over TCP/IP to a workstation computer holding Statnett s Synchrophasor Development Kit (S 3 DK), which provides a real-time data mediator that parses the PDC data stream and makes it available to the user in the LabVIEW environment. Also, the PDC streams the PMU data to a Compact Reconfigurable IO systems (crio) from National Instruments Corporation, programmed to (1) Receive and parse synchrophasor data streamed by the PDC using IEEE C protocol, (2) Map the data to IEC data model and (3) Transmit the synchrophasor data through either Routed-Sampled Value or Routed-GOOSE services defined in the IEC standard. The transmitted data is then parsed on a workstation holding a real-time IEC data parser which makes the PMU data available to the user in the LabVIEW environment. The selected PMU-based monitoring applications, presented in the deliverable 6.2, are then implemented on the workstation using the PMU data parsed by either the IEC or IEEE C data mediators. 31

113 EPS Static load Dynamic load 220 kv Core Core 2 Voltage regulator Wind farm PV farm Residential PV system CES Capacitor bank Circuit breaker FOP Recloser Fault Legend 36 kv Core Core 5 Core kv kv Core kv 729 Core Core 10 Core / kv kv IDE4L Deliverable 6.3 Annex A Figure 3.1 KTH laboratory infrastructure. Ref. Grid from T Core 7 Core Opal-RT Real-time simulator analog outputs (voltage/current) 36 kv Amplifier Synchrophasor Data (C ) PMU Processed Data Graphs Synchrophasor Data (IEEE C over TCP/IP) DMS computer WAN Synchrophasor Data (IEC over UDP/IP) RT-IEC / IEEEC data parser Data processing and extracting required components Monitoring functions From T6.3 PDC Synchrophasor Data (C ) IEC Gateway (External controllers - NI- crio 9076) Figure 3.2 Laboratory architecture used for demonstration of PMU applications. 32

114 Project no: Project acronym: IDE4L Project title: IDEAL GRID FOR ALL Deliverable 6.3: Emulation of the aggregator management and its interaction with the TSO-DSO Annex B Detailed Test Case Description Due date of deliverable: Actual submission date: Start date of project: Duration: 36 months Lead beneficial name: Tampere University of Technology, Finland Writers/authors: RWTH, IREC, KTH, DTU Dissemination level: Public

115 IDE4L Deliverable 6.3 Annex B Track Changes Version Date Description Revised Approved /07/2016 First complete version of the deliverable /07/2016 Comments and feedback collected and addressed /07/2016 Final revision and document finalisation Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero, Gerard del Rosario (IREC), Hossein Hooshyar (KTH) Gianluca Lipari, Ferdinanda Ponci (RWTH), Cristina Corchero (IREC) 2

116 IDE4L Deliverable 6.3 Annex B TABLE OF CONTENTS: 1 AGGREGATOR EMULATION IN REAL-TIME Unareti MV and LV networks LV customers/prosumers clusters definition MV profiles generation (winter scenario) LV profiles generation (winter scenario) Flexibility traded in the Market Agent (winter scenario) Available flexibility from CA s portfolio (winter scenario) Weather inputs (winter scenario) Electric energy price inputs (winter scenario) MV profiles generation (summer scenario) LV profiles generation (summer scenario) Assumptions on activated flexibility (summer scenario) Available flexibility from CA s portfolio (summer scenario) Weather inputs (summer scenario) Electric energy price inputs (summer scenario) MV profiles generation (mid-season scenario) LV profiles generation (mid-season scenario) Flexibility traded in the Market Agent (mid-season scenario) Available flexibility from CA s portfolio (mid-season scenario) SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION IEC gateway for transmission of PMU data: test on IEEE/IEC gateway traffic generation Steady state model synthesis of active distribution networks: test 1: reproduction of the equivalent model parameters in real-time Steady state model synthesis of active distribution networks: test 2: incorporating the effect of mutual inductances Steady state model synthesis of active distribution networks: test 3: model synthesis of a sample active distribution network Steady state model synthesis of active distribution networks: test 4: sensitivity analysis Voltage stability indicators: test 1: no DG connected to the distribution network Voltage stability indicators: test 2: DG connected to the MV section of the distribution network Voltage stability indicators: test 3: loss of generation in the transmission network

117 IDE4L Deliverable 6.3 Annex B 2.9 Small-signal dynamic model synthesis and stability indicators: test 1: mode estimation using centralized architecture Small-signal dynamic model synthesis and stability indicators: test 2: mode estimation using decentralized architecture

118 E23L0 1 IDE4L Deliverable 6.3 Annex B 1 AGGREGATOR EMULATION IN REAL-TIME 1.1 Unareti MV and LV networks In order to perform the experiments on a realistic simulated environment, a modelling of the physical network was made. For this, real customer and network data from UNARETI was used both in LV and MV levels. Medium voltage (MV) For the medium voltage network, only the relevant part to the case was modeled, this is the feeders E23L01, E23L02 and E23L03 connected directly to VIOLINO-E23 transformer on the 15 kv side. The used data from the network operator includes installed transformer characteristics, contractual power and line characteristics. A simplified diagram is shown in this figure: S W S W S W E23L0 3 S W E23L0 2 S W Figure 1.1 UNARETI MV network with additional PV units (winter and summer scenarios) For summer test purposes, 11 PV generation nodes of 500 kw each and 2 of 400 kw have been added. Additionally, one generation unit already exists at node 545 which has been assumed of PV type. The aim of these changes was to increase power feed-in from PV units in order to attain line saturation caused by relatively high share of renewable sources. MV/LV transformers feeding residential type customers, as well as commercial customers and P generators located in the same node were separated in different nodes connected by an ideal conductor, thus having a single entity for each one. 5

119 IDE4L Deliverable 6.3 Annex B Table 1.1 Number of MV nodes per type (MV/LV transformer, MV commercial and PV unit) Line Type Number of nodes E23L01 MV/LV Transformer 11 Commercial 1 PV Generator 6 E23L02 MV/LV Transformer 10 Commercial 1 PV Generator 0 E23L03 MV/LV Transformer 14 Commercial 6 PV Generator 8 Low voltage (LV) The low voltage subsection of the simulated network corresponds to the family of customers fed by the 1056 transformer. The list of nodes and their contractual consumption and generation values was obtained by linking the node ID and the contracted power datasheet through their mail addresses. Each LV node has been assigned a single customer with its aggregated consumption and generation. As some mismatches were detected, the following modifications were applied: Additional nodes were added for the customers not included in the node datasheet. The nodes were connected close to existing ones with similar mail address. All nodes without specified contractual power values were discarded. Customers with different addresses clustered in the same node were reallocated in new nodes connected through a new line to the same from node as the original one. Lines connecting the new nodes were added with same characteristics as the existing lines. The MV/LV transformer supplies the network through 10 feeders which are connected to the customers. There was no information available for 3 out of those 10 feeders thus it was assumed that no customers were attached. On the other hand, a group of customers whose node information was not available were allocated to feeder number 4 since aggregated measurements in such feeder revealed the existence of load. This allocation is assumed realistic since related customers are geographically close to the feeder. Table Number of LV nodes per type (real customers) Line Active Domestic Passive Domestic Non-Domestic Total 6

120 IDE4L Deliverable 6.3 Annex B FEEDER FEEDER FEEDER FEEDER FEEDER FEEDER FEEDER FEEDER FEEDER FEEDER LV customers/prosumers clusters definition The domestic customers in the LV part were clustered in four different categories according to their type: 0) Passive domestic customers with no flexibility 1) Active domestic customers (with PV) and enhanced flexibility capacity (storage and heating, ventilation, and air conditioning, HVAC) 2) Active domestic customers (with PV) and limited flexibility capacity (only HVAC) 3) Passive domestic customers with limited flexibility capacity (only HVAC) Table 1.3 Number of of LV nodes per type (modifications on real customers) Customer type Type 0 (Passive domestic no HVAC flexibility) Type 1 (Active domestic - with PV, storage and HVAC flexibility) Type 2 (Active domestic - with PV and HVAC flexibility) Amount per type Type 3 (Passive domestic HVAC flexibility) 41 Non Domestic no flexibility 29 It should be noted that part of the real LV customers were enhanced to provide flexibility for the scenario via the assumption of additional ESSs (energy storage devices) in their individual premises or by means of power 7

121 IDE4L Deliverable 6.3 Annex B modulation capability of their HVAC devices. On the other hand, original LV PV units owned by real active domestic customers were kept the same for the scenario. Only clusters 1, 2 and 3 are flexible resources and all of them are assumed to belong to the CA s portfolio. Graphically: PCC - + Load HVAC PV Storage Cluster 3 Cluster 2 Cluster 1 Figure Residential network users clustering. The node emulated in the experimental setup is assumed to belong to cluster 1, since it includes all the characteristic devices of its prosumers. 1.3 MV profiles generation (winter scenario) The MV/LV transformers active consumption was inferred based on historical records of the node 1056 from January 29 th to March 3 rd, The data, presented in irregular intervals of approximately 1,5 minutes and separated by feeders, confirmed that the feeders 5, 9 and 10 were not supplying any power. These measurements were aggregated in 15-minute intervals and the individual feeders were summated, obtaining the net consumption of the node For experimental purposes, the day with the highest consumption peak was chosen for the emulation, namely February 15 th of 2013 (Friday) from 19:00 to 19:30 The profile corresponding to this date was replicated on the rest of the MV/LV transformer nodes, scaling it according to the installed power of each transformer. Additionally, in order to generate the sufficient amount of congestion in the affected line, all transformer profiles were scaled up by a factor of 411/261. For the case of the MV clients, the shape of their consumption profile was taken from the global demand for northern Italy as specified on the Gestore dei Mercati Energetici database, scaling it accordingly to the contractual power of each consumer (being the top value of the profile as the 70% of the contractual power). The data, provided in an hourly fashion, was linearly extrapolated to 15-minute intervals. Generators were set at null output as there was no irradiation at the given time of that day (starting from 19:00). 8

122 IDE4L Deliverable 6.3 Annex B For the initial network operating conditions, a fault on the line connecting the node 1056 with the 15-kV busbar in Violino E-23 substation was assumed, which forced a network reconfiguration transferring all load from feeder E23L03 to feeder E23L01. This situation causes overload in the line between node 545 and the 15-kV busbar in Violino E-23 substation. Figure 1.3- Global demand of northern Italy in February 13th, Source: Gestore dei Mercati Energetici 9

123 IDE4L Deliverable 6.3 Annex B Figure Consumption of the node 1056 for February 15th, Source: UNARETI historical data 1.4 LV profiles generation (winter scenario) Based on historical measurements of 21 low-voltage active and passive customers, the individual profiles of all LV network users have been generated. Since the available data corresponded to the winter of year 2015 (unlike the MV measurements which correspond to year 2013), it has been assumed that the most reasonable day to choose would be February 13 th 2015 (also on Friday like February 15 th 2013). These historical values, presented in 5-minute intervals and divided by consumed and generated power, have been aggregated in 15-minute values and added as net consumption from the grid. Analogous to the MV/LV transformer profiles, the profiles associated to the LV customers have also been scaled by a factor of 411/261. After that, they have been clustered in active and passive profiles and have been assigned to a specific domestic customer cluster (as previously defined). Finally, power profiles of nondomestic users have been generated through heuristics so that the sum of the profiles matches the aggregated profile in bus The methodology used to generate the different LV profiles is explained in the following paragraphs. This algorithm has been developed to obtain the load profiles for every node in a LV distribution network considering the lumped LV load profile of all the feeders downstream the MV/LV substation and a small set of reference LV load profiles from customers connected to this network. Specifically, 21 profiles from individual consumers/prosumers provided by UNARETI were used as inputs. Such profiles were replicated and allocated to different clusters according to the methodology described below. The algorithm is implemented using MatLab. It comprises several steps. Analytically, the procedure steps are: 10

124 IDE4L Deliverable 6.3 Annex B Figure Flow chart First, all necessary data detailed in the following Table is loaded into the algorithm. Table Description of parameters NPRef kp NND ND na np cp ncpa ncpp cpn ncpn int LV_Profiles MV_Profile number of real profiles (reference profiles) kind of reference profile: 1 is prosumer, 0 is consumer number of non-domestic customers in the network number of domestic customers number of domestic prosumers number of domestic consumers vector of contracted power values for domestic customers (W) number of prosumers per value of contracted power number of consumers per value of contracted power values of contracted power for non-domestic customers (W) number of customers per value of contracted power number of time intervals real profiles (W) real profile of MV for the network (W) 11

125 IDE4L Deliverable 6.3 Annex B The algorithm begins with the partition of the LV profiles. The split criterion classifies the profiles which have negative values (associated with generation) as prosumers, and the rest of them as consumers. This is followed by a clustering within each partition according to the maximum power values. The k-means algorithm clusters the LV profiles into three groups; 1) clusters of customers with a contracted power of 3.3 kw, 2) clusters of customers with a contracted power of 4.95 kw, and 3) clusters of customers with a contracted power of 6.6 kw. In the second stage of the procedure, the generation of domestic profiles is done. In this step, load profiles are randomly generated per type of user (according to its flexibility and contracted power) for all the existing amount of customers per type of user in the network. Once all domestic profiles have been generated, prosumers and consumers are clustered according to: Figure Clustering of customers Finally, the diference between the MV profile and the sum of domestic profiles is allocated among the non domestic customers: 12

126 IDE4L Deliverable 6.3 Annex B Figure Profiles of non-domestic customers The surplus caused by LV domestic profiles is allocated among non-domestic prosumers (including generation). Similarly, the lack of energy (green area) needed to re-profile is allocated to non-domestic consumers. 1.5 Flexibility traded in the Market Agent (winter scenario) Flexibility offers and bids introduced in the Market Agent tool are assumed to be offered by three flexibility resources directly connected to MV network and one aggregation of LV residential flexibility resources connected downstream bus number 1056: Table 1.5 Flexibility bids and offers (MA inputs) Node Flexibility blocks quantity 1 (MW) price 1 ( /MWh) quantity 2 (MW) price 2 ( /MWh) quantity 3 (MW) price 3 ( /MWh) (CA) After the Market Agent selects the most economic flexibility offers and bids that solve the congestion during one 15-min period, the Tertiary Controller activates flexibility in nodes 297 and 1006, as well as the 13

127 IDE4L Deliverable 6.3 Annex B Commercial Aggregator (CA) with customers connected downstream bus This CA allocates the activated flexibility among its available individual customers. Despite being the cheapest offer, resource in bus 1512 is not activated since it is connected to feeder E23L02 and its flexibility does not contribute to relief thermal and voltage operational constraints in the network: Table 1.6 Activated flexibilities in winter scenario (MA output) Node Activated flexibility (MW) (CA) Available flexibility from CA s portfolio (winter scenario) Flexibility volume offered by the CA in the Market Agent tool (100 kw) needed to be coherent with the available flexibility assumed for clusters 1, 2 and 3 in CA s portfolio. It is worth noting that the real-time tool of the CA bases its flexibility allocation procedure among consumers/prosumers on their individual flexibility availability. Hence, such flexibility availability needed to be estimated, also according to the existing clusters and their respective facilities. Independent household and PV meter readings were available from all customers, and they were aggregated to generate one single baseline per user for the scenario. On the other hand, available up/downwards flexibility per user was estimated as the amount of power a network user can deviate from its baseline power consumption if needed by the aggregator. Flexibility from electrical heating has been estimated from simulations run, assuming dwellings located in Barcelona and built between the 50 s and 80 s. In this simulation, the maximum available flexibility from heating has been assumed as half of the difference between heat pump load with a thermostat set at 25 C and the same heat pump set at 20 C. Flexibility from household load has been assumed performing a simulation with the model of a dwelling with high penetration level of appliances. Finally, EES are assumed to be composed of one Tesla Powerwall home battery (3.3 kw, 6.4 kwh) connected to the user premises through an SMA Sunny Boy Storage 2.5 inverter (2.5 kw) thus limiting the charging and discharging power to 2.5 kw. Furthermore, it is assumed that the available flexibility from this ESS corresponds to a situation in which the battery is at 50% state of charge (SOC) at the beginning of the activation and is able to charge/discharge at full power for the entire activation period. 1.7 Weather inputs (winter scenario) Temperature and solar irradiation values have been retrieved from the ARPA Lombardia measurements database corresponding to the Corzano-Bargnano weather station, which is the closest to the city of Brescia that offers irradiation measurements. 14

128 IDE4L Deliverable 6.3 Annex B Figure Temperature profile of February 13th, Source: ARPA Lombardia Figure Solar irradiation of February 13th, Source: ARPA Lombardia 15

129 IDE4L Deliverable 6.3 Annex B It is worth noting that weather data is only needed for the configuration of the Energy Management System of the microgrid being emulated in IREC Energy Smart Lab, since this local management schedules optimal microgrid operation within a 24-hour time horizon. On the other hand, the winter case test emulates and simulates grid and microgrid operation in the time frame between 19:00 and 19:30, when no PV production takes place. 1.8 Electric energy price inputs (winter scenario) The existence of prosumers in the CA s portfolio, especially the ones managing storage systems (cluster 1), justifies the assumption of the existence of Energy Management Systems (EMS) at prosumers premises in order to optimize their local energy utilization, for instance, via energy arbitrage. For simplicity, only the EMS of the prosumer being emulated in the laboratory has been used for the tests. On the other hand, the LV profiles of the rest of simulated prosumers have been assumed as the power output resulting from the operation of their local management devices. The optimization module of the EMS minimizes the costs associated with the operation of the microgrid. Among these costs, the most relevant is the electric energy price at which the microgrid can buy or sell energy. This price is highly dependent on wholesale market prices and thus such information is needed for the functioning of the EMS. Results of MGP prices downloaded from Gestore dei Mercati Energetici for northern Italy have been used as price forecast inputs in the emulated microgrid. 1.9 MV profiles generation (summer scenario) The selection of a summer scenario in which excessive PV production causes local congestions has had to comply with simultaneous restrictions in order to be able to test the real-time CA tool in a realistic scenario: Relatively high PV production in relation to local demand: spring/summer months are suitable in principle Relatively high HVAC (air conditioning) load and subsequent temperature regulation ranges: summer months are preferred against spring months Only prosumers with storage systems would have been able to provide flexibility to the CA thus flexibility potential would have been limited to cluster 1 in that case. In order to expand contribution to clusters 2 and 3, a day with relatively high air conditioning consumption has been chosen: August 18 th The time with highest PV production is 13:00 and the test is executed within the timeframe ranging from 13:00 and 13:30. For the summer scenario, the profile for the node 1056 has been defined as the aggregated consumptions for all its connected LV customers. This profile has been replicated for the rest of the MV/LV transformers in the MV network and scaled according to their installed capacity. The client profiles have been estimated based on data from the Gestore Mercati Energetici database normalized to 1 per unit at the peak day value (August 18 th 2015). Nonetheless in this case a reduction of the consumption by a factor of 0,1 has been applied for testing purposes, since relatively low demand was needed to contribute to the generation of reverse power flows in combination with high PV production. For the photovoltaic generators, the historical solar irradiation and temperature was used in order to calculate the cell outputs according to an empirical formula obtained from measurements of a real PV unit using least squares methodology: 16

130 IDE4L Deliverable 6.3 Annex B P joker = max (((b 1 I + b 2 ) T cell + b 3 I + b 4 ) P max, 0) [W] c where T cell = a 1 + a 2 T air + a 3 I [ºC] and the model parameter values are a 1 = 17,23292 a 2 = 0, a 3 = 0, b 1 = 0, b 2 = 42,77774 b 3 = 9, { b 4 = 1885,868 For the congestion to appear, a fault in the line connecting bus 545 and the 15-kV busbar in the MV/MV substation (feeder E23L01) is simulated. This causes constraints in the line connecting bus 1056 and the 15- kv busbar in the MV/MV substation (feeder E23L03) due to high reverse flow from feeder E23L03 back to the substation. Figure PV power output per unit of installed power for August 18th,

131 IDE4L Deliverable 6.3 Annex B 1.10 LV profiles generation (summer scenario) Consumption for all customers has been estimated as the load demand of the region of northern Italy during August 18 th The peak value of this consumption profile has been scaled down to the 15% of the installed power of each consumer in order to obtain sufficiently low global consumptions so as to generate a reverse flow in the bus 1056 MV/LV transformer. The same formula used in the MV scenario has been applied to obtain the photovoltaic generation for active customers. Consumption and production has been lumped for each customer, which has resulted in all domestic network users provided with PV generation becoming net energy producers. Figure LV consumer load ratio according to its contractual power 1.11 Assumptions on activated flexibility (summer scenario) The Market Agent tool is currently designed for the trading of load reductions and generation increases (downwards flexibility). However, in the summer case it is assumed that congestion is addressed with upwards flexibility; this is by means of load increases and generation curtailment. Hence, this tool cannot be used to optimize the activation of flexibility in this case. Therefore, it has been decided to make some assumptions on the activated flexibility as long as they are technically reasonable. To this aim, the following procedure has been applied to assess the amount of flexibility activated per node: The reduction of reverse power flow needed to remove congestion in feeder E23L01 was equivalent to an increase of 260 kw of load. An amount of 100 kw of flexibility is activated from the CA resources connected downstream bus 1056 (like in the winter case) although such flexibility is of upwards type. 18

132 IDE4L Deliverable 6.3 Annex B The remaining flexibility needed ( = 160 kw) has been allocated to the same nodes that already provided this service in the winter case. The amount allocated to bus 297 is greater than the volume allocated to bus 1006 because its activation is more effective to solve the constraint: Table Activated flexibilities in summer scenario Node Activated flexibility (MW) (CA) Available flexibility from CA s portfolio (summer scenario) Similar to the winter case, two different types of flexibility sources have been assumed. First, temperature regulation of residential air conditioning systems has been assumed as flexible load and it is a characteristic common to all consumers/prosumers in CA s portfolio. Second, likewise the winter case, prosumers in the portfolio with available storage (cluster 1) have an additional flexibility of 2500 W based on the specifications of the Sunny Boy Storage inverter by SMA (upwards flexibility in this case) Weather inputs (summer scenario) For the summer case, the irradiation and temperature values needed to simulate PV production at all voltage levels also have been obtained from the ARPA Lombardia website. Furthermore, the same PV model has been used to simulate active power injections to the network. 19

133 IDE4L Deliverable 6.3 Annex B Figure Temperature profile in August 18th, Source: ARPA Lombardia Figure Solar irradiation in August 18th, Source: ARPA Lombardia 20

134 IDE4L Deliverable 6.3 Annex B 1.14 Electric energy price inputs (summer scenario) Likewise the winter scenario, results of MGP prices downloaded from Gestore dei Mercati Energetici for northern Italy (August 18 th, 2015) have been used as price forecast inputs in the EMS of the emulated microgrid MV profiles generation (mid-season scenario) The MV/LV transformers active consumption was inferred based on historical records of the node The data, presented in irregular intervals of approximately 1,5 minutes and separated by feeders. These measurements were aggregated in 5-minute intervals and the individual feeders were summated, obtaining the net consumption of the node For this scenario the day chosen for the emulation was November 25 th 2015, a mid-season working day in the morning, from 07:00 to 09:00. The profiles used for the MV buses where generated combining the single LV profiles randomly and the scaling the results according to the installed power of each transformer. Additionally, in order to generate the sufficient amount of congestion in the affected line, all transformer profiles were scaled up by a factor of four LV profiles generation (mid-season scenario) Based on historical measurements of 21 low-voltage active and passive customers, the individual profiles of all LV network users have been extracted. Then they have been aggregated according to the simplification used for the modeling of the grid. Analogous to the MV/LV transformer profiles, the profiles associated to the LV customers have also been scaled Flexibility traded in the Market Agent (mid-season scenario) Flexibility offers and bids introduced in the Market Agent tool are assumed to be offered by three flexibility resources directly connected to MV network and one aggregation of LV residential flexibility resources connected downstream bus number 1056: Table 1.8 Flexibility bids and offers (MA inputs) Node Flexibility blocks quantity 1 (MW) price 1 ( /MWh) quantity 2 (MW) price 2 ( /MWh) quantity 3 (MW) price 3 ( /MWh) (CA)

135 IDE4L Deliverable 6.3 Annex B After the Market Agent selects the most economic flexibility offers and bids that solve the congestion during one 15-min period, the Tertiary Controller requests the Commercial Aggregator (CA) the activation of a CRP. Then the CA allocates the activated flexibility among its available individual customers. Table 1.9 Activated flexibilities in mid-season scenario (MA output) Node Activated flexibility (MW) 1056 (CA) Available flexibility from CA s portfolio (mid-season scenario) Flexibility volume offered by the CA in the Market Agent tool (48.3 kw) needed to be coherent with the available flexibility available in CA s portfolio. Independent household and PV meter readings were available from all customers, and they were aggregated to generate one single baseline per user for the scenario. On the other hand, available up/downwards flexibility per user was estimated as the amount of power a network user can deviate from its baseline power consumption if needed by the aggregator. 2 SYNCHROPHASOR APPLICATIONS FOR ENHANCING TSO-DSO INTERACTION 2.1 IEC gateway for transmission of PMU data: test on IEEE/IEC gateway traffic generation In this study, a measurement location has been specified on the grid model that is simulated by the OPAL-RT simulator. Synchrophasors are sent to the PDC which streams the data over TCP/IP to the crio holding IEEE- IEC Gateway. On the workstation, the Receiver part of library receives the real-time streams of data in IEC format and parses the R-SV or R-GOOSE messages. Figure 2.1.A and Figure 2.2.A show the captured TCP/IP frame transmitting an IEEE C Data message respectively in R-GOOSE and R-SV traffic generation tests. In these frames, the phasors are transmitted in floating-point and rectangular format. It can be seen that the Data messages captured in Figure 2.1.A and Figure 2.2.A are consistent with the C Data message and its frame specification. The data message starts by common words of: 1) SYNC, 2) FRAMESIZE, 3) IDCODE, 4) SOC, 5) FRACSEC and ends by 21) CHK frame. The frames specific to Data messages are: 6) STAT, 7) PHASOR 1(Real), 8) PHASOR 1(Imag),..., 19) FREQ and 20) DFREQ. The detailed analysis of R-GOOSE message shown in Figure 2.1.B, confirms its conformity with the specification defined in IEC standard. In this figure, the bytes in blue background color constitute the complete R-GOOSE session layer encapsulated in the UDP/IP frame. The range of bytes assigned as (B) 22

136 IDE4L Deliverable 6.3 Annex B show the session header frames and the bytes assigned as (C) are the user data of session layer. The frames marked as group (D) are the goosepdu bytes. The detailed analysis of R-SV message shown in Figure 2.2.B, confirms its conformity with the specification defined in IEC standard. In Figure 2.2.B, the bytes in blue background color constitute the complete R-SV session layer. The range of bytes assigned as (B) show the session header frames and the bytes assigned as (C) are the user data of session layer. The frames marked as group (D) are the svpdu bytes. (A) IEEE C data message 23

137 IDE4L Deliverable 6.3 Annex B (B) IEC R-GOOSE message Figure 2.1 Wireshark captures analysis in Routed-GOOSE traffic generation test. (A) IEEE C data message 24

138 IDE4L Deliverable 6.3 Annex B (B) IEC R-SV message Figure 2.2 Wireshark captures analysis in Routed-SV traffic generation test. In order to evaluate the latency imposed by utilization of the developed Gateway, the test performed 40 times in each R-SV and R-GOOSE traffic generation cases. The process duration of each cycle in Gateway is calculated using the Wireshark captures on Gateway workstation. In each test, after 10 seconds of stable operation, the process time of 20 mapped messages are calculated. The results of the imposed latency for these 20 samples are presented in Table 2.1. Table 2.1 QoS test of gateway process time performance Bakken et al presented the requirements of synchrophasor data delivery for five category of power applications. In Table A2.1.2, the latency of the developed Gateway is compared with these five level of requirements. It can be seen that the effect of added latency due to utilization of the developed Gateway on end-to-end delay requirement is acceptable in most applications requiring maximum rates up to samples per second. 25

139 IDE4L Deliverable 6.3 Annex B Table 2.2 Comparison of the developed gateway process time in Windows OS with end-to-end delay requirements in five types of power applications Gateway 2.2 Steady state model synthesis of active distribution networks: test 1: reproduction of the equivalent model parameters in real-time A simple equivalent model as shown in Figure 2.3, is included in a power system model (not shown here) and is simulated for 100 s with known parameters to reproduce the values of the parameters by the SSMS application. The PMU measurements were available on both buses of the model. The true values of the a a a a parameters on phase a of the model are R p. u., X p. u., E 1 p. u., and rad. In this test, it has been assumed that there is no mutual coupling between the phases. Two Events were created in the simulation: 1) a load of (0.2+j0.1) p.u. is connected at t = 40 s in the right side of PMU2 (Event occurs outside the bounded section by the PMUs) and, 2) a step increment of 10 % is a introduced in E at t = 70 s (the Event occurs inside the bounded section by the PMUs). Figure 2.4 shows the reproduced model parameters for phase a of the model by the implemented SSMS application. It is evident from Figure 2.4 that for Event 1 there are no noticeable changes in the model parameters as it was outside the boundary of the two PMUs; whereas Figure 2.4). a E is updated accordingly for Event 2 (see top right of 26

140 IDE4L Deliverable 6.3 Annex B V 1 a <δ 1 a V 2 a <δ 2 a I 1 a <φ 1 a 3 I 2 a <φ 2 a V 1 b <δ 1 b 3 V 2 b <δ 2 b I b b 1 <φ 1 V c c 1 <δ 1 I c c 1 <φ 1 Any feeder configuration with an arbitrary combination of load and DG I b b 2 <φ 2 V c c 2 <δ 2 I c c 2 <φ 2 PMU1 PMU2 V 1 a <δ 1 a V 1 b <δ 1 b V 1 c <δ 1 c I 1 a <φ 1 a I 1 b <φ 1 b I 1 c <φ 1 c R a R b R c X a X b X c V 0 a <δ 0 a V 0 b <δ 0 b V 0 c <δ 0 c R a R b R c X a X b X c I 2 a <φ 2a I 2 b <φ 2b I 2 c <φ 2 c V 2 a <δ 2 a V 2 b <δ 2 b V 2 c <δ 2 c I0 c <φ0 c I0 b <φ0 b I0 a <φ0 a The reduced steady state equivalent model R a X a R b X b R c X c E a <δ a E b <δ b E c <δ c Figure 2.3 Synthesized model based on two PMU measurement points. Figure 2.4 Reproduction of the parameters of phase a of the equivalent model by LabVIEW SSMS application. 27

141 IDE4L Deliverable 6.3 Annex B 2.3 Steady state model synthesis of active distribution networks: test 2: incorporating the effect of mutual inductances In this test, it is shown how the existence of the mutual coupling between the phases will affect the estimated value of the model parameters. For this test, three different models are simulated: Model 1: Equivalent model without any mutual inductance between the phases (same as Test 1) Model 2: Same as Model 1 with addition of mutual inductance between the phases. The values of ab ac bc the mutual inductances are X p. u., X p. u., and X p. u. Model 3: The parameters of Model 2, estimated by the SSMS application, are inserted to an equivalent model, shown in Figure 2.3. The voltages and currents at the PMU measurement points of Model 2 are then compared with those of the Model 3 for the purpose of validation. Note that Model 2 is the true model with mutual coupling whereas Model 3 is a synthesized model of Model 2. Figure 2.5 shows the estimated values of the parameters of phase a of Model 2. As shown in the figure, the a a a a estimated values of the per phase parameters, i.e. R, X, E, and, are different from the real values due to the existence of the mutual coupling between the phases. Also, In order to better show the effect of mutual coupling, a load of (0.2+j0.1) p.u. is connected to phase b at t = 40 s. As shown in the figure, although the event is taking place on phase b, it impacts the values of the parameters on phase a due to the existence of the mutual coupling. Figure 2.6 compares the voltage and current magnitude and angles of PMU2 for all the three models. As shown in the figure, the voltage phasor is noticeably different if the effect of the mutual couplings is not considered (compare Model 1 with Model 2/Model 3). Also, the figure shows that Model 3 reproduces the voltage and current phasors of Model 2 quite accurately, which validates the accuracy of the parameters of Model 3 that is estimated by the SSMS application. Figure 2.5 Estimation of the parameters of phase a of Model 2 (Test 2). 28

142 IDE4L Deliverable 6.3 Annex B Figure 2.6 Voltage and current phasor of PMU2 in Model 1, Model 2, and Model 3 (Test 2). 2.4 Steady state model synthesis of active distribution networks: test 3: model synthesis of a sample active distribution network In this section, the SSMS application is applied on the IDE4L reference grid, presented in the deliverable Error! Reference source not found.. Figure 2.7 depicts the single-line diagram of the reference grid. PMU measurements were made available by connecting the Opal-RT simulator in HIL, on Node 814 and Node 852. Two Events were created in the simulation: 1) A lateral MV feeder disconnects at Node 834 at t = 40 s, and 2) wind farm generation of 1 MW (0.2 p.u.) disconnects at Node 854 at t = 70 s. The equivalent model parameters for phase a, estimated by the SSMS application, are shown in Figure 2.8. As the figure shows, the disconnection of the lateral feeder (Event 1) mainly impacts the value of a R and a X. This is because when the lateral feeder disconnects, the currents flowing through all phases of the main feeder reduce accordingly which, in turn, decreases the voltage drop induced on all phases through the mutual coupling. In case of Event 2, the disconnection of the wind farm, located inside the bounded section of the two PMUs, causes a E to drop from p.u. to 0.93 p.u. 29

143 IDE4L Deliverable 6.3 Annex B Figure 2.7 Single-line diagram of the IDE4L reference grid. Figure 2.8 Estimation of the parameters of phase a of the sample network (Test 3) In order to validate the equivalent model parameters, estimated by the SSMS application, the bounded section of the reference grid has been replaced by an equivalent steady state model, as shown in Figure 2.9. The same Events, performed on the reference grid, have been simulated on the grid, shown in Figure 2.9. During the simulation, the parameters of the equivalent model have been updated. Figure 2.10 and Figure 2.11 compare the voltage and current phasors, provided by PMU1 and PMU2 in the reference grid, with those of the grid containing the equivalent model. Note that the oscillations in the phasor signals are due to the sinusoidal variation embedded in the static load models of the simulated grid. As the figure shows, the reproduced voltage and current phasors are quite similar to those of the reference grid, which shows the validity of the developed SSMS application. In order to analyze the difference between the true values and 30

144 IDE4L Deliverable 6.3 Annex B the reproduced values, mean error is calculated for each sections of the simulation i.e. e1= before Event1, e2=between Event 1 and Event 2, and e3=after Event 2. The calculated errors are shown on Figure 2.10 and Figure As shown in the figures, for both PMUs, the maximum error of voltage magnitudes, current magnitudes, voltage angles, and current angles are p.u., p.u., rad., and rad., respectively. Figure 2.9 Single-line diagram of the grid with the equivalent model. Figure 2.10 True phasors versus reproduced phasors for PMU 1 (Test 3). 31

145 IDE4L Deliverable 6.3 Annex B Figure 2.11 True phasors versus reproduced phasors for PMU 2 (Test 3). 2.5 Steady state model synthesis of active distribution networks: test 4: sensitivity analysis Similar to the previous test, the IDE4L reference grid has been used as the benchmark, in which the windfarm generation at node 854 (shown in Figure 2.7) decreases from 1 pu to 0 pu in 10 steps of 0.1 pu. Before and after each power variation step, a set of parameters have been estimated for the equivalent model. Therefore, for 10 variations in windfarm generation, 11 set of parameters have been estimated. The section bounded by the PMUs in the active distribution network has been replaced by an equivalent model as shown in Figure 2.9. The reduced equivalent model has been simulated using the parameters obtained by the SSMS application, which are updated at the start of the each power variation and held constant up to next variation. The equivalent model parameters for phase a, estimated by the SSMS application, are shown in Figure 2.12 and Figure As it can be seen in Figure 2.12, for each step decrease in wind power generation, R increases while X decreases in order to compensate for the change in wind power. The decrease in wind generation causes the voltage to be dropped; this can be seen from top part of Figure The bottom part of Figure 2.13 shows that although the phase angle of the voltage source does not change so much with the change in wind power generation, but still it decreases in steps same as voltage magnitude. 32

146 IDE4L Deliverable 6.3 Annex B Figure 2.12 Estimated parameter R and X (Test 4). 33

147 IDE4L Deliverable 6.3 Annex B Figure 2.13 Estimated parameter E and δ (Test 4). Figure 2.14 and Figure 2.15 compare the voltage and current phasors, provided by PMU1 and PMU2 in the active distribution network, with those of the network containing the equivalent model. As the figures show, the reproduced voltage and current magnitude of their respective phasors are quite similar to those of the sample network. However, phase angles of voltage and current for the PMU 1 and PMU 2 have a minor estimation error, which is due to the fact that the value of the phase angle are more sensitive to the change in active power in the system. Va (p.u.) true reproduced Ia (p.u.) a (rad) a (rad) Time (sec) Figure 2.14 True phasors versus reproduced phasors for PMU 1. 34

148 IDE4L Deliverable 6.3 Annex B Va (p.u.) true reproduced Ia (p.u.) a (rad) a (rad) Time (sec) Figure 2.15 True phasors versus reproduced phasors for PMU 2. Variants of the Total Vector Error (TVE) are used as a metric for the evaluation of the results obtained by comparing the output phasor for active distribution model and reduced equivalent model. TVE is defined as the measure of the difference between the theoretical phasor value of the signal being measured and estimated phasor by PMU. We define three different types of TVEs whose details are as follows: 1. PMU TVE: The difference between the true phasor in the simulation environment and the phasor estimated by PMU is defined as PMU TVE as shown in Figure This TVE is primarily due to phase angle errors product of instrumentation channels and improper timing sources of the PMU. In our experimental setup, real-time simulator and PMUs have different dynamic range of their voltage and current ratings; this may be a source of PMU TVE. In addition, the current amplifiers as shown in Figure 2.16 (B) are sources of phase angle errors, which in turn may cause PMU TVE. 2. Field Application TVE: The difference between the phasor estimated by the PMU and the phasor reproduced by the synthesized model is defined as Field Application TVE as shown in Figure This type of TVE is product of the SSMS application estimation errors. 3. End-to-End TVE: The difference between the true phasor in simulation environment and the phasor reproduced by the synthesized model while going through the whole instrumentation and data acquisition chain (i.e. hardware-in-the-loop setup) is defined as End-to-End TVE as shown in Figure This is the sum of PMU TVE and Field Application TVE. 35

149 IDE4L Deliverable 6.3 Annex B Each of the above mentioned types of TVEs are shown in Figure 2.16 (A) in vector form, whereas TVEs are shown in Figure 2.16 (B) in hardware-in-the-loop lab setup to differentiate the origin of different types of TVEs in the whole experimental chain. Estimated phasor End-to-End TVE by field Application Field Application TVE PMU TVE True phasor Estimated phasor by PMU (A) Types of TVEs End-to-End TVE Field Application TVE PMU TVE Opal-RT Simulator KF+SSMS PDC Stream PMU1 Current Amplifier Field Applications S 3 DK real-time data mediator SEL-PDC 5073 PMU2 Grid model is simulated in real-time (B) Hardware-in-the-loop setup Figure 2.16 Origins of different TVEs in hardware-in-the-loop setup In this sensitivity analysis example, the End-to-End TVE is calculated using (1) (which is the sum of PMU TVE and Field Application TVE) and results are tabulated in Table 2.3. T VE( n) = ( Vˆ ( n) - V ( n)) + ( Vˆ( n) - V ( n)) 2 2 r r i i 2 2 V ( n) + V ( n) r i (1) where Vˆ ( n ) = Real part of the reproduced (estimated) voltage r 36

150 IDE4L Deliverable 6.3 Annex B Vr ( n ) = Real part of the true (actual) voltage Vˆ i ( n ) = Imaginary part of the reproduced (estimated) voltage V ( n ) = Imaginary part of the true (actual) voltage i As mentioned before, 11 parameter sets are estimated for 10 variations in wind generation output. For each set of parameters, the End-to-End TVE is calculated for voltage and current phasors for both of the PMUs as shown in Table 2.3. We define a 5 % error requirement for the End-to-End TVE in this study. The basis of this requirement is as follows: Power flow models available in literature can have more than 15.7 % error. As SSMS is a method to synthesize steady state models of the active distribution network, having 5 % End-to-End TVE for estimated parameters still provides much better accuracy of the reduced equivalent models than power flow models. 5 % is an acceptable value because End-to-End TVE contains PMU TVE, which is not related to the performance of the target application. Specifically, in our case, the amount of phase angle error generated by instrumentation, difference in dynamic range of the ratings of different equipment and the error generated by improper timing sources increases the End-to-End TVE. In our experimental setup, the KF feeds the data to SSMS applications. KF application itself may introduce an estimation error that may increase overall End-to-End TVE The End-to-End TVE is summarized in Table 2.3. It can be observed in Table 2.3 that the voltage phasor of PMU 1 is estimated within the desired margin, having a maximum End-to-End TVE is 0.31 %. The voltage phasor of PMU 2 has a higher phase angle error than PMU 1, causing the maximum End-to-End TVE to reach 1.41%. The maximum End-to-End TVE for current phasor of PMU 1 is 3.49 %, this is because the estimation error for phase angle of current phasor for PMU 1 was high, causing higher value for End-to-End TVE. For current phasor for PMU 2, the maximum value for End-to-End TVE is 1.36%. Table 2.3 shows that the maximum End-to-End TVE for the both voltage and current phasors for PMU 1 and PMU 2 is less than our 5 % error requirement for End-to-End TVE for steady state model synthesis method. Table 2.3 End-to-End TVE for different wind power variation for PMU 1 and PMU 2 End-to-End TVE (%) V I V I

151 IDE4L Deliverable 6.3 Annex B 2.6 Voltage stability indicators: test 1: no DG connected to the distribution network For all case studies the LV network (6.6 kv) is treated as an aggregated load, connected to the secondary bus of the MV/LV substation, as shown in Figure A PMU (PMU2) has been installed at this position as the voltage stability of this bus is of interest in this study. Also, as shown in the figure, another PMU (PMU1) is installed at the PCC between the distribution network and the transmission network. Figure 2.17 Location of PMUs on the IDE4L reference grid for the VSA application. In this case, neither the MV nor the LV section contains distributed generations and all loads connected to the distribution network are fed from the transmission network. The results of this case study, demonstrating the effect of each network on Instability Index (ISI) and PV curves at the bus of interest (where PMUs is installed), are shown in Figure

152 IDE4L Deliverable 6.3 Annex B Figure 2.18 Real-time simulation results of Test 1. 39

153 IDE4L Deliverable 6.3 Annex B 2.7 Voltage stability indicators: test 2: DG connected to the MV section of the distribution network In this test a wind turbine unit producing 1 MW and 0,5 MVAR is connected to the MV grid. All other conditions are the same as those in Test 1. The simulation results are demonstrated in Figure Figure 2.19 Real-time simulation results of Test 2. Comparing the results from this test with the first one, it is obvious that the added distribution generation to the MV section has boosted the voltage stability at the bus of interest. Although, this improvement could be realized through analysis based on traditional simple model (Thevenin equivalent), our developed application not only can clearly identify the improvement on the voltage stability distribution network but also it shows what and where was the cause of improvement. In comparison with Figure 2.18, in Figure 2.19 the whole ISI has been decreased which means improvement in the voltage stability (increment of maximum power on PV curve also means enhancement in voltage stability). The distinguishing point is that PV curve representing the transmission network has not been changed, but the PV curve that shows distribution network effect has been changed considerably. This change clearly shows that the stability improvement has been initiated from MV network and transmission network has no role on it. 40

154 IDE4L Deliverable 6.3 Annex B 2.8 Voltage stability indicators: test 3: loss of generation in the transmission network In this test, all conditions are the same as those of Test 2, however the wind farm that was connected to the transmission network is disconnected. The results for this test are presented in Figure Comparing the results from this test with those of Test 2, it can be inferred that although total stability has not been changed significantly, the transmission effect on the voltage stability of the bus of interest has deteriorated due to the disconnection of the wind farm at the transmission level. Figure 2.20 Real-time simulation results of Test 3. 41

155 36 kv IDE4L Deliverable 6.3 Annex B 2.9 Small-signal dynamic model synthesis and stability indicators: test 1: mode estimation using centralized architecture In this test, the estimation algorithm is at the center receiving data from different PMUs placed at different locations, as shown in Figure It estimates single set of modal parameters for the whole system. Core 6 36 kv EPS 220 kv Core PMU Core kv 834 Core 3 Core 5 PMU Core 4 PMU4 6.6 kv Core 7 Core / PMU kv Legend 720 Static load Dynamic load Voltage regulator Wind f arm PV f arm Residential PV system CES Capacitor bank Circuit breaker FOP Recloser Fault + - Figure 2.21 IDE4L reference grid for mode estimation application testing and validation. To identify the frequency of oscillation present in the reference grid, the grid was simulated in real-time using the real-time simulator. Voltage and current signals were recorded at various points throughout the grid. The recorded data was evaluated by Fourier analysis in Matlab. The results calculated based on the signals such as voltage magnitude, voltage angel difference and current magnitude showed presence of a 0.42 Hz inter-area oscillation present throughout the grid. Fourier estimates of some of the recorded signals are shown in the figure below (Figure 2.22). 6.6 kv 729 Core Core Core kv

156 IDE4L Deliverable 6.3 Annex B i ii iii iv Figure 2.22 Frequency spectrum plots showing the presence of 0.42 Hz inter-area oscillation at various measuring points. Figures i, ii, iii and iv correspond to PMU1, PMU2, PMU3 and PMU3 respectively. Also, a forced oscillation of 1.7 Hz was imposed in terms of variation in active power of a load at node 799 in the LV section (seen at the core 7 in the Figure 2.21). To confirm the presence of the forced oscillation, same step as above was repeated to find the fourier estimates using saved data from various parts of the grid. The natural inter-area and forced local oscillations can be seen at the fourier plots presented in Figure

157 IDE4L Deliverable 6.3 Annex B i ii iii iv Figure 2.23 Frequency spectrum plots showing the presence of 0.42 Hz inter-area oscillation and forced oscillation at various measuring points. Figures i, ii, iii and iv correspond to PMU1, PMU2, PMU3 and PMU3 respectively. The presence of local forced oscillation is seen in the spectrum generated from data received from PMU3 and PMU4. In the centralized mode estimation test, positive sequence voltage phasor magnitudes from four PMUs across the network were fed to the application. The application processed all the received data streams (channels) together to give single set of mode estimates. A screenshot of the estimated modes on the frequency-damping ratio plane is shown in the Figure The inter area mode around 0.42 Hz is clearly identified. The forced oscillation of 1.7 Hz is also detected to some extent. 44

158 IDE4L Deliverable 6.3 Annex B Figure 2.24 Screenshot of centralized mode estimation window showing inter-area oscillation at 0.42 Hz and forced oscillation at 1.7Hz. Estimates were calculated on a moving window of 6000 samples of the recorded time series data. The estimated results were stored in an excel file for further analysis. Probability Distribution Function (PDF) plots, based on the estimates obtained is presented in Figure 2.25 and Figure Figure 2.25 PDF plots for frequency and damping ratio of the inter area oscillation (mode 1) at 0.4 Hz using centralized estimation architecture. Figure 2.26 PDF plots for frequency and damping ratio of the forced oscillation (mode 2) at 1.7Hz using centralized estimation architecture. The PDF plots also suggest the high density of estimates around 0.42Hz modes. The damping ratio detected ranges from 5% to 1% range. The forced oscillation is detected although the pdf plot suggests that the density of estimates around the 1.7 Hz forced oscillation is less. The next test case demonstrates the results from the decentralized mode estimation which tries to improve the identification ability of local forced oscillation (mode 2). 45

159 IDE4L Deliverable 6.3 Annex B 2.10 Small-signal dynamic model synthesis and stability indicators: test 2: mode estimation using decentralized architecture In decentralized mode estimation test, positive sequence voltage phasor magnitudes from four PMUs across the network were fed to the application. The application processed all the received data streams (channels) together to give multiple set of mode estimates. In this case the application gave one set of estimates for each incoming PMU stream. These Probability Distribution Function plots of estimates from data from different PMUs showcase the location specific insights provided by decentralized architecture Figure 2.27 Left: Screenshot of decentralized mode estimation window showing inter-area oscillation at 0.42 Hz and forced oscillation at 1.7Hz. For comparison, screenshot of centralized mode estimation window is shown in the right. Figure 2.28 PDF plots for frequency and damping ratio of modes 1 and 2 utilizing PMU1 data using decentralized estimation architecture. 46

160 IDE4L Deliverable 6.3 Annex B Figure 2.29 PDF plots for frequency and damping ratio of modes 1 and 2 utilizing PMU2 data using decentralized estimation architecture. Figure 2.30 PDF plots for frequency and damping ratio of modes 1 and 2 utilizing PMU3 data using decentralized estimation architecture. 47

161 IDE4L Deliverable 6.3 Annex B Figure 2.31 PDF plots for frequency and damping ratio of modes 1 and 2 utilizing PMU4 data using decentralized estimation architecture. It can be observed from Figure 2.28 to Figure 2.31, as we move from the PMU1 voltage magnitude data to PMU4 voltage magnitude data, the estimates for the forced oscillation becomes better. Figure 2.32 compares the PDF plots for estimated results around 1.7 Hz forced oscillation to show the improved efficacy in estimation of forced oscillation utilizing PMU4 data in the decentralized architecture. Figure 2.32 Comparison of PDF plots for frequency estimates around 1.7 Hz forced oscillation (mode 2) using centralized and decentralized estimation architecture. Left figure presents result from centralized estimation while the right figure presents results utilizing PMU4 data in the decentralized architecture. The above results from the two test cases showed that the application was able to identify the modes present in according to the time-series data received by them. The results proved that decentralized architecture provides a more local and high-resolution awareness in mode observation than the centralized estimation process. Decentralized mode estimation architecture in a more advanced form could be employed by the TSOs to develop a double layered and modular oscillation monitoring and data exchange framework which could be employed seamlessly between multiple local electricity utilities. 48