Real-time Monitoring of the EPFL Campus Distribution Network using PMUs

Transcription

1 Real-time Monitoring of the EPFL Campus Distribution Network using PMUs C-DAX is funded by the European Union's Seventh Framework Programme (FP7-ICT ) under grant agreement n Herman Bontius Alliander Paolo Romano EPFL i-pcgrid Workshop March 26 th, 2015

2 Power Delft TU Substation Automation IEC Protection, Control, Communication SCADA Network Control Grid Ops High Voltage Design & Engineering WAMS WAMPAC Situational & Security Awareness Accenture Smart Grid Services EALA Europe, Africa, Latin America ABB T&D / Automation KEMA T&D Consulting ENECO Infra / Joulz Quanta Technology Accenture bas.kruimer@accenture.com

3 Outline C-DAX Project Introduction The evolution of Active Distribution Networks (ADNs) Alliander strategy to manage ADNs A PMU-based approach to operate ADNs The EPFL campus MV grid test-bench The System Architecture Phasor Measurement Unit (PMU) Phasor Data Concentrator (PDC) Real-time state estimator (RTSE) Conclusions & Future Work i-pcgrid Workshop

4 C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids C-DAX Consortium C-DAX is funded by the European Union's Seventh Framework Programme (FP7-ICT ) under grant agreement n

5 C-DAX Project EC FP7-ICT call project C-DAX: Cyber-secure Data And Control Cloud for power grids Duration: Total budget: Euro EU-funding: Euro Project coordination: Alcatel-Lucent iminds / Alliander Project website: Project partners C-DAX communication middleware Enables power systems applications to exchange information Implements information-centric networking (ICN) paradigm Targeted use cases Real-time state estimation based on PMU measurements Retail Energy Transactions Encrypted Cloud Communication MV-PMU measuring principles C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 5

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8 Smart Grid Communication patterns Smart grid applications require support for diverse communication models: 1-to-1: e.g. control messages for specific assets 1-to-M: e.g. energy offers in demand response schemes M-to-1: e.g. energy consumption reports in demand response or smart metering M-to-N: e.g. multiple charging offers from different charging stations to multiple EVs Anycast communication: e.g. receiving an offer for voltage regulation by any suitable subset of EVs located in a certain area Asynchronous communication: e.g. EVs can only retrieve/deliver data while connected to the network C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 10

9 ICN Information Centric Networks Point-to-point networks Producer of information pushes data to predefined consumers via explicit point-to-point connections ICN paradigm Consumers pull or subscribe to the data they need regardless of who produced the information, or when, or where it is stored Data is collected in topics Advantages: Inherent security as network and physical locations of hosts are not exposed (publish subscribe communication) Overlay network takes care of managing the connections, optimal placement of the data within the cloud, resilience ICN allows in-network management and processing of information, e.g., innetwork caching of frequently used data, aggregation, filtering, rate adaptation, optimal traffic management based on underlying communication infrastructure C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 11

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14 Prototype Purpose Validation of baseline communication functionalities and basic failure management of C-DAX Validation of security framework Validation of IEEE C protocol adaptation layer Environment IEEE 34 Bus as power grid topology PMU measurement data provided by EPFL Virtual Wall network test bed provided by iminds RTSE application by EPFL Monitor Monitor Bus1Node PDC Adapter SubClient RTSE LabView Security Server Resolver LAN PMU-Bus1 PubClient Bus3Node PMU-Bus3 PubClient BaseStation Base Station Bus4Node PMU-Bus4 PubClient 16 Virtual Wall Bus7Node PMU-Bus7 PubClient Bus1 Bus3 Bus4 Bus7 C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids

15 Laboratory validation Real-time state estimation of the targeted electrical network PDC PDC C-DAX cloud PMU PMU PMU PMU Real-time model of the electrical grid C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 17

16 Field Trial Purpose Deploy C-DAX software in an existing distribution grid Evaluate applicability of C-DAX under realistic conditions Environment Distribution grid provided by Alliander including a solid and fast IP network PMUs provided by National Instruments RTSE application by EPFL C-DAX software Time plan Deployment of PMUs and C-DAX software: late 2014 Scheduled start of field trial 2015 Alliander s MS Livelab National Instruments PMU for MV level Source: Alliander N.V. Source: National Instruments Sweden C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 18

17 Validation results Test network consisting of: 1 primary substation 16 secondary substations 5 PMU s for full feeder observability 19

18 Evolution of Active Distribution Networks (ADN s) HV Characteristics: Bidirectional power flows Ultra-short term volatility 63% MV 2 sec LV PV output power [ pu.] time [hours] i-pcgrid Workshop

19 Managing Alliander s Distribution Networks We have : Very reliable power service (CML<20min/yr) 99% underground cabled Relatively low capacity (1.3 kw/p residence), heating/cooling is Natural Gas powered (today). Heavily regulated /owned by local government We face : High penetration private EV CP s Residential PV is promoted Increasing of heat-pumps penetration How to cope with increasing dynamics and unpredictable power flows in MV/LV? i-pcgrid Workshop

20 Alliander Strategy No regret options: Analytics : Forecasting customer behavior and grid behavior, Demand Side Management: Using prosumer flexibility, local balancing, curtailment options Measure & Control DN ADN: Distribution Automation, Smart Metering, PMU MV monitoring? And Grid Reinforcements!! i-pcgrid Workshop

21 A PMU-based approach to operate ADNs Monitoring infrastructure components: = Phasor Measurement Unit (PMU) = Feeder Monitoring, Control Unit (PDC+RTSE+control+protection) i-pcgrid Workshop

22 The EPFL campus smart-grid project An ideal laboratory: 40 buses MV grid (20 kv line-to-line); 30 MW peak load; 6 MW peak CHP; 2.5 MW peak PV; 1 MW peak, 0.5 MWh Li-Titanate storage system; DSM to be deployed in two buildings i-pcgrid Workshop

23 The system architecture (1 st equipped feeder) i-pcgrid Workshop

24 F-Class PMUs for Distribution Networks < 0.1% TVE TVE 1% < Magnitude 0.1% Mag. error < 0.1% TVE < 0.05 Phase error Design requirements : High-accuracy High-speed TVE << 1% Harmonics/dynamics rejection High reporting rates Reduced latencies i-pcgrid Workshop

25 EPFL PMU prototype Synchrophasor estimation algorithm: IpDFT-based Spectral interf. compensation Sliding window DFT technique (MSDFT) compactrio-based PMU prototype: FPGA-based prototype Extreme determinism Low latencies (30 ms) Reporting rates up to fps (typically reduced to 50 fps) [1] P. Romano, Enhanced Interpolated-DFT for Synchrophasor Estimation in FPGAs: Theory, Implementation, and Validation of a PMU Prototype", Dec i-pcgrid Workshop

26 PMU measurements accuracy assessment Comments: TVE max = % TVE avg = % FE max = FE avg = RFE max = RFE avg = i-pcgrid Workshop

27 Substation setup 0.1 class sensors + PMU C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 29

28 Real-time State Estimation (RTSE) of ADN s Real-time State Estimation (RTSE): Process of estimating the network state (i.e., phase-to-ground node voltages) with an extremely high refresh rate (typically of several tens of frames per second) enabled by the use of synchrophasor measurements. Advantages of adopting RTSE processes in ADNs: Implicitly reduces the number of measurement point (and the installation costs) Enables real-time network monitoring Improves measurement robustness Application fields of RTSE in ADNs: Optimal Voltage/Power control Congestion management Optimal dispatch of Distributed Energy Resources (DER) Fault detection and location Network islanding i-pcgrid Workshop

29 Static (LWLS) vs. Dynamic (DKF) Linear RTSE Network topology PMU Measurements Static SE Estimated state Network topology Process model PMU Measurements Dynamic SE Prediction Estimation Estimated state Error distributions of the estimated magnitude and phases of the network state per bus and per phase, with reference to the adopted DKF with Q matrix assessment and LWLS. [2] Zanni, L.; Sarri, S.; Pignati, M.; Cherkaoui, R.; Paolone, M., "Probabilistic assessment of the process-noise covariance matrix of discrete Kalman filter state estimation of active distribution networks, Aug i-pcgrid Workshop

30 Integrated Bad-data Detection Process 1. Compare expected measurements with actual ones 2. Discriminate between anomalies (bad data) and faults (fast dynamics) 3. Take proper countermeasures 4. Replace bad-data with predicted ones Missing data [3] Pignati, M.; Zanni, L.; Sarri, S.; Cherkaoui, R.; Le Boudec, J.-Y.; Paolone, M., "A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs, Aug i-pcgrid Workshop 2015 Phase drift due to GPS disconnection 32

31 System performances Latency PMU Telecom PDC+RTSE Signal acquisition Synchrophasor estimation Data encapsulation Network delay Data-frame alignment State estimation t1 (30ms) t2 (8ms) t3 ( 1-15ms) t4 ( 1-3ms) t5 ( 20ms) t6 ( 1ms) Time Total latency: 61 ms (mean) 1.8 ms (std) Refresh rate: 20 ms i-pcgrid Workshop

32 Conclusions and Future Work We have built a novel monitoring infrastructure for ADNs and validated it in the EPFL campus MV network. The system is composed by advanced PMUs and a central unit that concentrates the data and estimates the system state. Within the next months the same system will be transplanted in one of the MV feeder of the Alliander network in the Netherlands. Measurements and state estimator outputs are publicly available online together with the repository of the historical data. They are accessible via smartgrid.epfl.ch. i-pcgrid Workshop