Real-Time Hardware-in-the- Loop Co-Simulation Platform for Microgrid Analysis

Transcription

1 Real-Time Hardware-in-the- Loop Co-Simulation Platform for Microgrid Analysis Martine Chlela, Carlos Rangel, Geza Joos McGill University Electric Energy Systems Laboratory 5-8 September 2017

2 Outline 2 Context & Motivation Real-Time HIL Co-Simulation Platform Microgrid Controller Testing Comprehensive Cyber Security Analysis Conclusion

3 Context & Motivation 3 Energy challenges in modern power grids include: Increased integration of intermittent renewable energy Integration of distributed energy resources (DER) Balance supply & demand Two-way flow of power & information Meeting economic & environmental constraints To overcome the challenges smart grids should rely on information & communication technologies (ICT) to provide: Real-time sensing & measurement Advanced control capabilities Remote maintenance & monitoring

4 Context & Motivation 4 The IEEE P2030 defines 3 interoperability architecture perspectives (IAP) that together comprise the smart grid: Power System IAP - power elements & their interoperability Communication Technology IAP - communications elements & networks Information Technology IAP - information flows, entities & protocols used to exchange that information A co-simulation platform needs to be developed to enable: Detailed modeling, seamless interfacing & synchronization of the 3 constituting layers to operate as a single entity Implementation & testing of advanced control algorithms Analysis of the interactions between the layers & comprehensive analysis of cyber security (cyber-attack modeling, impact assessment & mitigation strategies)

5 Co-Simulation Platforms 5 Many platforms were developed to interface the electric grids power system, information exchange & communication network layers: K. Hopkinson, et al. - EPOCHS W. Li., et al. - VPNET V. Liberatore, et al. - Powernet Major drawbacks of these setups include: Information is exchanged in the inter-synchronization period, leading to an accumulation of errors jeopardizing the fidelity of the simulations, especially the time-critical ones Lack of scalability not suitable for the modeling of large scale power networks Do not operate in real-time Do not provide a detailed modeling of either the power system or communication network components

6 Developed Real-Time Co-Simulation Platform - Key Features 6 Study of multiple scenarios in near real conditions & without risk Fast accurate & reliable implementation without introducing artificial delays Seamless interfacing of the power system, communication network & information layers causing no accumulation of errors Hardware-in-the-Loop (HIL) capabilities with sub microsecond time steps & communication protocol implementation capability Optimized grid models using advanced decoupling techniques & flexible for a variety of applications Highly scalable could be easily extended to model larger grids Adaptable to technological developments in engineering & configurations

7 Co-Simulation Platform Constituting Layers RTS1- Microgrid Feeder & DERs XS1 X S2 RTS2- Microgrid EMS Microgrid Controller EMS 7 EPS 25 kv PCC DERs Modeling Renewable DERs (WTG, PV) ESS 150 kw Diesel Generator 100 kw/ 25 kwh 25 kv/ 600 V kva WTG ESS DIESEL THERMAL LOAD Primary power management control loops Voltage & frequency regulation 320 kw/ 400 kva Applicable in grid-connected & islanded mode 320 kw/ 400 kva 25 kv/ 600 V kva PEI PEI PEI SG IEC publishers/subscribers 600 kw ±150 kvar 25 kv/ 600 V kva Measurement Devices Communication Network AnalogIn/ AnalogOut Controls IEC publishers/ subscribers Analog I/Os Communication network Medium for information exchange between microgrid & EMS Switched network providing 2-way flow of information Information exchange IEC messaging protocol TCP/IP Analog I/Os Secondary energy management system (EMS) Receives microgrid measurements & evaluates dispatch points & command to operate loads & DERs Implemented on a digital controller Requires a communication network for information exchange

8 8 Real-Time HIL Co-Simulation Setup Host Computer 2 Host Computer 3 Host Computer 1 running OPNET SITL Node 2 SITL Node 1 NI crio Digital Controller OPAL-RT RTS 2 Microgrid Controller EMS Analog I/O TCP IP Network OPAL-RT RTS 1 Microgrid feeder and DERs TCP Host Computer 4 running EMS optimization script & LabView Analog I/O EMS Controller Commands & dispatch Measurements Publisher for commands RTS EMS Subscriber for commands RTS Microgrid feeder & DERS Subscriber for measurements Publisher for measurements

9 Microgrid Controller EMS Formulation Optimization objectives & performance metrics 9 Objectives 1 Reduction of energy cost & liters of fuel consumed 2 Reduction of the amount of diesel energy used 3 Minimization of total load curtailed 4 Increase the capacity of hosting renewable energy & reduction of power & energy violations Metrics Total fuel consumed Net diesel generator energy Net cost of energy dispatched by the diesel generator Average cost of using the diesel generator Unit Liters kwh $ $/kwh

10 Microgrid Controller EMS Formulation Creation of wind speed, wind power & load demand forecast for 2 days 10 Set up the size of the wind power, demand response & load Selection of the moving forward window time in hours Creation of the reference power data Initialization of the parameters (SoC of ESS) Algorithm decisions NO End program YES Elapsed time >= 24 hrs? Send dispatch results of the current time period to controller Send the information back to the optimization engine (Matlab) Send dispatch commands through analog channels to emulated network Receive measurements from the microgrid

11 Diesel Generator power (kw) ESS power (kw) Load & Wind Profiles (kw) Microgrid Controller EMS Testing Pwtg Pload 11 Power (kw) Time (Hours) Time (hrs) Pdiesel Pdiesel (offline) 60 Pess Pess (offline) Diesel Power (kw) Power ESS (kw) Time (Hours) Time (hrs) Time (Hours) Time (hrs)

12 Microgrid Controller EMS Testing SOC (kwh) ESS SOC (%) 12 SOC (offline) SOC Time (Hours) Time (hrs) Amount of diesel consumed (L) Diesel energy consumed (kwh) Diesel generator operational cost ($) Diesel energy cost ($/kwh) Real-Time Offline

13 Cyber-Attacks Modeling Attacks compromising data integrity Attacks compromising availability 13 False Data Injection Attacks (FDI) P DER_FDI i t = P DER i t + B (t A ) Distributed Denial-of-Service (DDoS) Attacks P DER DDoS k i + j t = P DER k i 1 t s. t. i 1 t < t A i t & B t = B if t t A 0 elsewhere s. t. for j = 0, 1,, T A t i 1 t < t A i t, k = 1,.., n

14 14 Cyber-Attacks Implementation Host Computer 2 Host Computer 3 Host Computer 1 running OPNET SITL Node 2 SITL Node 1 NI crio Digital Controller OPAL-RT RTS 2 Microgrid Controller EMS Analog I/O TCP IP Network OPAL-RT RTS 1 Microgrid feeder and DERs TCP Host Computer 4 running EMS optimization script & LabView Linux Host Computer 5 launching cyber-attacks

15 Cyber-Attacks Implementation 15 Wireshark capture for a FDI cyber-attack

16 FDI Attack Impact Quantification 16 ESS power (kw) Frequency (Hz) W/O mitigation W/ mitigation Time (s) W/O mitigation W/ mitigation Time (s) Diesel generator power (kw) Load power (kw) W/O mitigation W/ mitigation Time (s) W/O mitigation W/ mitigation Time (s) Synchronous machine s slow dynamics & system low inertia cause the small disturbances to result in large frequency excursions Transient & steadystate instability Unnecessary activation of protection schemes Loss in reliability & cost

17 DDoS Attack Impact Quantification 17 ESS power (kw) Diesel generator power (kw) W/O mitigation W/ mitigation Time (s) W/O mitigation W/ mitigation Time (s) Load power (kw) Frequency (Hz) W/O mitigation W/ mitigation Time (s) W/O mitigation W/ mitigation Time (s) Loss of communicated commands & measurements As load & generation mix vary, DERs local controller cannot compensate to ensure balance Lack of coordination between resources Large excursions & unnecessary activation of protection schemes Loss of reliability & uneconomic operation

18 Multi-Stage Cyber-Resilient Control Strategy 18 Microgrid Controller EMS Main Grid PCC Controllable Loads Renewable DERs Diesel Generator Energy Storage System

19 DDoS FDI Comprehensive Cyber Security Analysis Min/Max Frequency (Hz) Renewable Energy Shed (kwh) Load Not Served (kwh) Average Cost of Energy ($/kwh) S1 No Control 36.8 / S1 - Control 59.3 / S2 No Control 13.4 / S2 - Control 59.9 / S1 - No Control 42.1 / S1 - Control 59.3 / S2 - No Control 8.2 / S2 - Control 59.9 / The multi-stage control infrastructure provides: Enhanced resiliency against cyber-attacks Transient & steady-state stability Lower cost of operation Higher microgrid ability to host renewable energy & supply critical loads

20 Potential Uses of the Platform The proposed co-simulation platform evaluated the performance of a microgrid controller EMS & analyzed cyber security mainly from a power system perspective Although the platform models microgrid systems, it has all the building blocks to be easily extended to model large power grids Amongst many, other applications of the co-simulation platform include: Testing the performance of different communication technologies & protocols Evaluating the effectiveness of recommended cyber security practices & guidelines applied at the communication network layer to ensure resiliency Conducting other power system studies (protection, EV charging & energy management algorithms, demand response ) 20

21 Conclusions Detailed modeling of the real-time HIL co-simulation setup, its constituting power system, information exchange & communication network layers & their interfacing was presented 21 An EMS microgrid controller has been formulated & its performance were validated Cyber security studies have been conducted to model cyber-attacks, assess their impact & propose mitigation solutions The benefits of the platform have been detailed and the broad range of applications to which its suitable were presented

22 THANK YOU Martine Chlela Carlos Rangel Electric Energy Systems Laboratory, McGill University