Enabling the Resilient Electric Grid Anurag K Srivastava Washington State University (anurag.k.srivastava@wsu.edu) PSERC Webinar November 3, 2020 1
The electrical distribution network is reliable but not resilient Extreme events demands superior grid Increase in extreme Aging infrastructure Changing grid with resilience weather events DERs and Edge Devices 4
What is Resilience? • Multiple definitions exist. • Focus on critical loads for distribution grids. • "Resilience – Ability of the system to supply its critical loads, even in the presence of multiple contingencies". FERC: The ability to withstand and reduce the magnitude and/or duration of disruptive events, which include the capacity to anticipate, absorb, adapt to, and/or rapidly recover from such an event. PES PSDP definition and metric for resilience WG, PES T&D Distribution System Resiliency, PSOPE tools for resilience, AMPS 5 Resilience Metrics and Evaluation Methods and CIGRE WG 4.47 and 2.25
Monitoring and Enabling Resilience Distribution Network Advanced Distributed Sensors Control Mobile Microgrids Generators Real-Time Resilience Management System (RT-RMS) Control Center 6
RT-RMS Computation Engine REST Decision User Interface Application Support Power Flow Analysis Interface Geospatial Analysis Visualization Data Requisition Threat Modeling and Forwarding Monitoring Resilience Analysis Data Formatting Resource Management Optimization Analysis and Visualization Database COVID-19 Data Existing System SCADA Designed Generator Load µPMU Network Weather GIS Data Information Data Resilience Analysis Tool – Functional Block Diagram 7
Monitoring Resilience 8
Multi-temporal Multidimensional Resilience Framework EVENT TIMELINE R = f(A,W,R) ANTICIPATE WITHSTAND RESTORE • How well is • How well can • How quickly the system system can system prepared for continue to recover from the supply event and predicted critical loads continue impact of an during supply to incoming event? critical event? loads? And at what cost? BEFORE EVENT AFTER EVENT DURING EVENT Based on determining all the system factors impacting system ability to provide energy to the critical loads and integrating all the factors for AWR 9
Anticipate Metric “Know” Variables • Earthquake/Tsunami Early Warning System • Crew/Fleet Availability • Static Weighted sum “Know” Fuel Reserve • Measures Score Load Profile • Time of Year Risk* • BESS SOC* Weighted sum “Predict” Real Time Measures • Score Cybersecurity Best Practices • Weather Data • Threat Warning System • Cyber Asset Score • Spare Inventory Normalization • Crew/Fleet Schedules Threat Modeling and Impact • Fuel Prediction Anticipate Metric “Predict” Variables • Wind induced failure • Inundation induced failure • Communication Asset Failure • Avalanche Risk
Anticipate Metric Anticipate Metric Threat Preparedness and ID Power Delivery and Loads Resources for Restoration Supporting Cyber Infrastructure Vulnerability 80 Have threats for the system Backup Communication Critical Cyber assets 1 Are critical loads identified? Energy storage installed identified? Installed? identified 70 Access to communication to Percentage of High Priority Impact of threat analyzed Automatic Restoration plan crew and state agencies? Multi-user clearance for 2 critical loads that have local and documented? in place Message center, Emergency critical cyber assets backup generation 60 Radio System Percentage of Medium Backup of all electronic data Is emergency response Priority critical loads that Has a restoration plan drill Firewall audit performed in 3 in case of loss of internet Resilience Scores 50 curated for each threat? conducted in the last year? the last week have local backup service generation Average warning time Average runtime of backup Is repair teams on standby 4 Cyber threats identified Digital asset inventory 40 before threat generation to be deployed Cross training of crew for CEC Staff undergone Accuracy of warning for each Fault prevention plan in Average time for cyber 5 handling all multiple cybersecurity practices 30 threat place? blackstart equipment repairs training Staging site selected for Has drill conducted for Has vegetation management 6 20 triage,storm trailers, mobile Access control review Anti-virus Installed threats in the last year? performed in the last year? restoration command center Anticipated maximum hours Is complete asset inventory PPE and tools for restoration Employee password Content Management 7 10 of outage for threats available crew authentication System Installed Is there a routine inspection Fuel inspection. Does fuel Virtual Private Network 8 plan available for system storage have polishers IP Rules for access control credential review 0 assets installed? Tsunami Earthquake Avalanche Volcano Mutual assistance program Static IP configuration for Are there a backup control Average blackstart 9 with neighboring CEC servers and network Scenarios restoration time center? cooperatives connected equipment Are there data backup and Average downtime of each 10 archiving plans for critical Third party access control generator due to threat data?
Withstand Metric RESILIENCE SCORES 1.2000 E-scenario1 1.0000 E-scenario2 E-scenario3 RESILIENCY VALUE 0.8000 E-scenario4 E-scenario5 0.6000 R-scenario1 R-scenario2 0.4000 R-scenario3 R-scenario4 0.2000 R-scenario5 0.0000 1 2 3 4 5 6 TIME (HR)
Recover Metric ‘Know’ variables Real Time Measures • • Grid Topology Automated Switches • • AMI Load Not Lost • • Cyber Asset Fuel Reserve • • Spare Inventory Water Head Level • • Fuel BESS SOC* Recovery Capability Topological factors Weighted sum • Topological resiliency score ”Topological” factors • Available generation • Critical Load not lost Recovery Efficacy • Critical path redundancy Weighted sum • Critical Load Fraction “Operational factors” and Operational factors “Economical factors” • Number of Switching operations Recovery Schemes Score • Switching time Normalization • Load Curtailment Economical factors • Repair time Recover Metric • Repair lost
RT-RMS Tool: Decision Support 14
Enabling Resiliency Long term Medium Term Proactive Post Event Corrective Planning: Control Control Grid Hardening Damage/ outage estimation Mobile Generators (underground cable) Restoration using sensor Vegetation Transmission Network information, trouble call Management and system, weather information, Segmentation Outage Prediction loads Distribution Automation (Switching, Distribution automation and Sensor Deployment Sectionalization), and DERs Coordination Islanding Upgrade Poles and Microgrid with changing Temporary Microgrids Infrastructure boundary Backup/ mobile generators, Storage Deployment of Moving Networked microgrid and Automatic Generators Switches Distributed Backup Power Support to Optimization/ Control Crews and Logistics deployment individual critical facilities at Edge 15
Source: Cleantech.com
Event Specific Technical Challenges Tsunami: Isolate to be Flood: Elevating Avalanche: Deploy crew impacted generators substation, flood sufficiently in advance to apriori to minimize hardened control room ensure their safety restoration time Wildfire: Vegetation Cyber-events: management, power Storm: Strengthening Distributed approaches, lines burial to minimize poles with guy wires, reduced reliance on the probability of fire power lines burial communication network induced by power lines Operational environment and different events -- There is no silver bullet 17
18 Use Case 1: Data-driven Distribution System Reconfiguration using D-PMU • D-PMUs can help us proactive reconfiguration of the system • Based on the measurement we can deploy pre-event reconfiguration with controlled islanding and using shift-and-shed of loads • Minimize impact of expected outage by pre-event shift-and-shed of loads
Algorithms: Distribution System Reconfiguration using D-PMU 19
Distribution System Reconfiguration using D-PMU 20
Distribution System Reconfiguration using D-PMU Resiliency Indices Comparison: Networked Microgrids Feeder Specific Resiliency Metrics 21
Proactive Control for Economic and Resilient Operation with BESS Resilient mode of operation Economic mode of operation Resilience analysis of RE- ESOT operations 1. Pandey, Shikhar, Srivastava, Anurag K., Srivastava, Sanjeev, Mohanpurkar, Manish U., Kandaperumal, Gowtham, and Hovsapian, Rob. ”Optimal Operation for Resilient and Economic Modes in an Islanded Alaskan Grid:” Preprint. IEEE PES GM 2020, Montreal, Canada August 2 – 6, 2020 22
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