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2.4_Overview of Microgrid Research, Development, and Resiliency Analysis_Hovsapian_EPRI/SNL Microgrid


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Presentation from the EPRI-Sandia Symposium on Secure and Resilient Microgrids: Overview of Microgrid Research, Development, and Resiliency Analysis, presented by Rob Hovsapian, Idaho National Laboratory, Baltimore, MD, August 29-31, 2016.

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2.4_Overview of Microgrid Research, Development, and Resiliency Analysis_Hovsapian_EPRI/SNL Microgrid

  1. 1. Overview of Microgrid Research, Development, and Resiliency Analysis Rob Hovsapian, Ph.D. Manager, Power and Energy Systems Idaho National Laboratory EPRI-Sandia Symposium on Secure and Resilient Microgrids August 29th , 2016
  2. 2. Core Capabilities of Power & Energy Systems Department • Facilities for accurate real-world model development for power system dynamic analysis • High fidelity test environment to test models based on real-world data in real-time for de-risking device integration. • 10-20 nanosecond scale simulation for power electronic dynamics • Control hardware in the loop and rapid prototying of controllers. • Advanced control technologies and decision making strategies Differentiating Capabilities • Front-end controller development • Multi-agent protection systems and reconfiguration schemes • Multi-agent adaptive control • Aggregators • PMUs • Relays & protection devices • Inverters Real-Time Digital Simulation of Power Systems Control Systems and Advanced Protection Devices and Systems Integration • µs-scale simulation of grid / microgrid events • Co-simulation of transmission-distribution- microgrid communication in power systems simultaneously • Calibrate protection hardware settings in real- time prior to field deployment. • Fuel Cells • LT and HT Electrolyzers • Microgrids • Computational Science • Energy and Storage Technologies Related INL Core Competencies • Power & Energy Systems • Advanced Control Systems Collaboration with Academia & Industry Using unique laboratory infrastructure to create a holistic ecosystem for developing, testing, and deploying power system technologies • Electric Vehicles and Fuel Cell Electric Vehicles • Pumped Storage Hydro • Supercapacitors • Batteries Energy Storage WSU CSU FSU HSU Real-time Grid Scenario Analysis Advanced Controls Ancillary Services Grid Stability Resilient Microgrid
  3. 3. Energy Systems IntegrationEnergy Conversion First Principles Research EV Holistic Systems Engineering Approach for solving next generation energy challenges INL Power & Energy Systems focuses on investigating power-grid problems using real-time models, develop advanced controls and strategies to mitigate the identified problems, and de-risk integration of variety devices to the microgrid / power grid. PV Battery Super Capacitor Wind Turbine Pumped Storage Hybrid Power Grid • Models based on real-world data in real-time • Physics-based modeling • Novel protection schemes and algorithm Energy conversion & storage • Thermal • Mechanical • Electrical • Chemical • Nuclear Grid Integration of • Electrical Vehicles • Supercapacitors • Flywheels • Pumped Storage Hydro • Batteries & Electrolyzers Pumped-storage Hydro for Integrating Multiple Run-of-the-river Concentrated Solar Power Safe and Efficient Integration of Grid Devices to Existing Power Grid IMPACTS & TAKEAWAYS Physics model-based approach towards solving power grid problems in real-time help mimic real- world conditions with high accuracy. Research on integrating industrial hydrogen production to enable better demand response and grid stability by integration of electrolyzers Electrical-Mechanical-Thermal cosimulation capability involving Pump-storage hydro, Concentrated Solar Power integrated with power grid. Real-time testbed enables Transmission, Distribution and Communication co-simulation for investigating cybersecurity vulnerabilities Electrolyzer integration for demand response and grid ancillary services
  4. 4. EMTP / RTDS Simulator INL Energy Systems Laboratory’s Demonstration Complex and Test Bed • For the renewable technologies – Modeling, simulation, and hardware-in-the-loop capabilities for demonstrations and dynamic analysis • Energy farms / microgrids • Integration power & energy systems • Control and integration strategies • Coupling with energy storage 4 Fuel Cell
  5. 5. Microgrid Management System (μGMS)! 5 A μG is a modified power distribution network that can be a part of the grid or independently generate, distribute, and regulate the flow of electricity to meet consumer demands. It can operate either grid connected or islanded and, if required, can switch between the two. μGMS is a specially-designed software tool that interacts with utility signals & coordinates communication between μG components in order to meet microgrid objectives. Creative Commons graphics courtesy Siemens
  6. 6. Microgrid & μGMS Objectives
  7. 7. INL Current Utility Microgrid Projects  Funded by California Energy Commission’s Electric Program Investment Charge  PON-14-301  Program Goal: Demonstration of Low Carbon-Based Microgrids for Critical Facilities  Partners – INL, Siemens, Tesla (Utility scale Storage) Humboldt University, PG&E
  8. 8. California Energy Commissioner – Project Future & Existing Energy Infrastructure
  9. 9. One-Line Diagram of 12 kV Line Joining Service Transformers at the Casino, Hotel and Admin Office Bldg Future Renewable generation sources:  Solar PV Plant 0.25 MW  Battery 0.2 MW Existing Load and Generation: •Estimated peak load is approx 0.7 MW •Estimated average load is approx 0.5 MW •Diesel generator for base generation 1 MW •Fuel cell + biomass 0.175MW
  10. 10. CEC- Project Architecture and Functionality Testing via CHIL Microgrid Modes of Operation: 1. Gridconnected 2. Black start transition 3. Off-grid operation 4. Resynchronization toPG&E network PG&E Power System Network INL Blue Lake Rancheria , CA Siemens MGMS Modbus/DNP3.0 connection
  11. 11. Microgrid Research, Development and System Design
  12. 12. Integrated CHIL & HIL Microgrid Test Environment I/OBus CERTS Microgrid CommunicationLayer IECProtocols(IEC61850) Real Time Digital Simulator (RTDS) Controller-Hardware-In-the-Loop (CHIL) Hardware-In-the-Loop (HIL)
  13. 13. Standard Resilience Terms • Resilience Withstand attacks, Recover from attacks, Adapt to changing conditions, Prevent future attacks proactively. • Resilience Quantification Codifying the methods and approaches of studying, operating and designing resilient microgrid. • Resilience Metric A “number” that eases comparison, optimization to implement most resilient configuration. • Resilience Framework Generalization of approaches & metric so that all distribution systems can be assessed using this technology
  14. 14. Difference between Resilience & Reliability Metrics 14 Reliability metrics: measure of “implosions” • Power system disruptions due to operational limitation of utility, machinery damage, momentary outages. • Does not consider events which are not fault of utilities (like, superstorms) • Computed over long time durations Resilience Metrics: measure of “explosions” • There are several natural and man-made threats constantly being made to circumvent ordinary protection systems and disrupt power system operation. • Considers external events that disrupt power system operation • Can be computed for near-term, real-time (operational), or over long time durations (planning)
  15. 15. DER Cyber-vulnerability Analysis Testbed (DER-CAT) RTDS Geographically Distributed Simulation for Larger Power Systems TCP/IP RTDS at Remote Sites at INL Dynamic Power System Model Co-Simulation Environment with Hardware-in-the-Loop RTDS Ethernet Power Hardware Control Hardware Allows cyber-vulnerability testing Ethernet Dynamic Power System Model Simulation Environment DER Controller DER Monitoring NS-3 Simulator
  16. 16. Test Scenario 1: DER Interconnection Distribution System Modeling Integration of DER to the Utility System Study the additional communication requirements due to DER integration Use DER-RAT to compute cyber- physical resiliency of the network Developed and modeled on DER-CAT Compare base case with cost-benefit analysis of the test condition
  17. 17. Test Scenario 2: Slow Oscillation Attacks • Slow Oscillations between two interconnected power systems are hard to detect, or easy to ignore. • Repeated slow oscillation can be used to create unprecedented harmonics in the system leading to blackouts Two- Area Interconnected Power System Modeling in DER-CAT Integration of DER to the Power System Simulate <1 Hz oscillations between the two areas of the system through interconnected DER manipulation Use DER-RAT to compute cyber- physical resiliency of the network Simulate conditions leading to unstable power swings DER Integration DER Integration < 1 Hz oscillations
  18. 18. Test Scenario 3: Bad Data Injection • Malicious Data can be injected at HV, MV, or LV of the power system. • Corruption of PMU Data concentrator can lead to wide-spread control failure of the power system Use DER-CAT to create coupled transmission and distribution networks Integration of DER to the Dist. System Manipulate data obtained through RTDS measurements (or HIL PMU), and DER generation variables in real-time Use DER-RAT to compute cyber- physical resiliency of the network Run Bad-data detection algorithm RAT
  19. 19. Test Scenario 4: Demand Response Hack DR Signal • Increase in DR signal and TOU pricing interactions with customers • Vulnerabilities in communication with customer Use DER-CAT to create coupled transmission and distribution networks Integration of DER to the Dist. System Manipulate DER Generation & load consumption behavior of consumers to create less than conducive grid loading conditions Use DER-RAT to compute cyber- physical resiliency of the network Study Power System dynamics against unwarranted consumer action
  20. 20. Test Scenario 5: Critical Load Restoration Despite Denial of Service (DoS) Attack Use DER-CAT to create coupled transmission and distribution networks Perpetrate DoS attack to a critical load Load and Frequency Control of Power System despite Attack Use DER-RAT to compute cyber- physical resiliency of the network • This study will focus on the dynamic performance of a power system during Denial-of-Service (DoS) attacks on (i) critical loads, and (ii) load frequency control (LFC) of smart grids.
  21. 21. – Microgrids (islanded configuration) have significant dynamic and transient swings due to low inertia – Real-time simulators (EMTP) allow an accurate modeling and assessment of such challenges – Real-time simulators allow microgrid models to interface • MGMS as Controller-Hardware-In-the-Loop (CHIL) • Power devices as Power-Hardware-In-the-Loop (PHIL) – A unique way of controller rapid prototyping, functionality, interoperability, & interconnection testing of MGMS – A systematic resilience framework that can analyze and quantify threats is critical 21 Observations and Way Forward
  22. 22. Thank you 850-339-9432