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Grid Resilience and Intelligence Platform (GRIP) Use-case

Grid Resilience and Intelligence Platform (GRIP) Use-case. CEC Advanced Simulation Program TAC Meeting Oakland, California 6 September 2019. Grid Resilience and Intelligence Platform (GRIP) Use-case. Alyona Ivanova (SLAC) CEC Advanced Simulation Program 6 September 2019

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Grid Resilience and Intelligence Platform (GRIP) Use-case

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  1. Grid Resilience and Intelligence Platform (GRIP) Use-case CEC Advanced Simulation Program TAC Meeting Oakland, California 6 September 2019

  2. Grid Resilience and Intelligence Platform (GRIP) Use-case Alyona Ivanova (SLAC) CEC Advanced Simulation Program 6 September 2019 This presentation was prepared with funding from the California Energy Commission under grant EPC-17-046. SLAC National Accelerator Laboratory is operated for the US Department of Energy by Stanford University under Contract No. DE-AC02-76SF00515

  3. Outline • Introduction • Objectives • Resilience overview • Anticipation • Pole model • Pole degradation • User Interface • Absorption • Model Overview • Planned Improvements External datasets Import & Validate (OpenFIDO) CSV Files External models Simulate (HiPAS & GLOW) Analyse (custom) Plots Shared datasets Copy/link (Cloud Security) Acknowledgements The GRIP project is funded by the US Department of Energy under the Grid Modernization Laboratory Consortium (GMLC). MySQL Data Shared models

  4. Grid Resilience and Intelligence Platform (GRIP) Applies artificial intelligence and machine learning to anticipate, absorb and recover from extreme events that affect the Distribution Energy System

  5. GRIP Innovation and Impact • GRIP applies AI and ML for grid resilience. • National impact with platforms and analytics • Facilitates streamlining use of ML/AI applications for distribution resources. • Final deliverable: open-source commercially available product. • Year 1: Anticipation (Completed) • Year 2: Absorption (In-progress) • Year 3: Recovery (Future Work)

  6. Anticipation Objectives • Determine use-cases for resilience. • Asset and protective device location and mapping • Predicting vulnerabilities to extreme weather conditions • Switch re-configuration • Predicting ferroresonance occurrences • Secondary voltage optimization with DERs • Vegetation management • Optimized work plans considering budget hardening options • Develop a new platform based on pre-existing Google tools. • Use previously DOE funded projects (VADER, OMF) as basis for GRIP • Test and validate anticipation solution with data and models provided by National Rural Electric Cooperative Association (NRECA).

  7. Core GRIP Simulation Analysis • GridLAB-D implementation incorporates vulnerability analysis • Analytical pole and line vulnerability model using weather data • Calculation of pole vulnerability index, electrical fault propagation and restoration time • Wind stress simulation represents worst case scenario • Support for arbitrary vulnerability simulations • User ability to specify the electrical system model • Calculations of stresses loading to pole failures • Cables tension • Pole-mounted equipment • Pole tilt angle • Wind and ice loading

  8. Internal pole degradation model • Applicable to wood poles only • Propagation of internal core degradation to outer edges • Degradation defined by minimum shell thickness • End-of-life thickness: 2” • Characterized by the difference between the outer core and inner core moment calculations • Accounts for pole base failures 2”

  9. IEEE Standard test models IEEE 123 IEEE 8500 Image obtained from DOI: 10.1109/TSG.2013.2288868 Image obtained from DOI: 10.1109/TDC.2010.5484381

  10. GRIP Further Design

  11. Absorption • Dynamic reconfiguration of the network into virtual islands after distribution circuit damage due to weather disturbances • Maximize amount of load that can be maintained during the fault • Uses DG, batteries, flexible DERs (water heaters, distributed batteries, EV chargers) when supporting virtual islands • Model development (SLAC/GridLAB-D) + Controls (Packetized Energy) + UI (Presence) • Typical sequence: • (1) Fault occurs • (2) Fault isolation • (3) Reconfiguration (Virtual Islanding) • (4) Load balancing

  12. Node representing the Bulk Grid 001 VIRTUAL ISLANDING TEST CASE built in GridLAB-D 1 NC NC NC 4 2 002 Legend NC Switch or circuit breaker/recloser that is closed (hot) 3 201 101 301 Switch or circuit breaker/recloser that is open (not hot) 5 7 9 102 202 302 Solar2 Solar3 Solar1 xyz Distribution circuit node (or collection of nodes) with (eg) hundreds of customers. 6 10 8 12 11 103 203 303 NO NO Fault location Battery1 Battery3 Battery2

  13. Node representing the Bulk Grid 001 CASE 1: SINGLE FEEDER FAULT 1 NC NC NC 4 2 002 Legend NC Switch or circuit breaker/recloser that is closed (hot) 3 201 101 301 Switch or circuit breaker/recloser that is open (not hot) 5 7 9 102 202 302 Solar2 Solar3 Solar1 xyz Distribution circuit node (or collection of nodes) with (eg) hundreds of customers. 6 10 8 12 11 103 203 303 NO NO Fault location Battery1 Battery3 Battery2

  14. Node representing the Bulk Grid 001 CASE 1: STEP 1BREAKER TRIPS 1 NC NC NC 4 2 002 Breaker opens Legend NC Switch or circuit breaker/recloser that is closed (hot) 3 201 101 301 Switch or circuit breaker/recloser that is open (not hot) 5 7 9 102 202 302 Solar2 Solar3 Solar1 xyz Distribution circuit node (or collection of nodes) with (eg) hundreds of customers. 6 10 8 12 11 103 203 303 NO NO Fault location Battery1 Battery3 Battery2

  15. Node representing the Bulk Grid 001 CASE 1: STEP 2FAULT ISOLATION 1 NC NC NC 4 2 002 Legend NC Switch or circuit breaker/recloser that is closed (hot) 3 201 101 301 Switch or circuit breaker/recloser that is open (not hot) 5 7 Sw. opens 9 102 202 302 Solar2 Solar3 Solar1 xyz Distribution circuit node (or collection of nodes) with (eg) hundreds of customers. 6 10 8 Sw. opens 12 11 103 203 303 NO NO Fault location Battery1 Battery3 Battery2

  16. Node representing the Bulk Grid 001 CASE 1: STEP 3RECONFIGURATION 1 NC NC NC 4 2 002 Legend Switch or circuit breaker/recloser that is closed (hot) 3 201 101 301 Switch or circuit breaker/recloser that is open (not hot) 5 7 9 102 202 302 Solar2 Solar3 Solar1 xyz Distribution circuit node (or collection of nodes) with (eg) hundreds of customers. 6 10 8 11 12 103 203 303 Fault location Battery1 Battery3 Battery2

  17. Planned Improvements • Planned enhancements to the pole vulnerability model • Add effect of changing wind direction • Add ice build-up model and line loading effects • Extend taxonomy of impacts of vegetation on lines, poles, and equipment • Pole degradation • Pole top failures due to equipment, weather and animal impacts

  18. Vegetation Risk Assessment Risk modeling and quantification questions related to vegetation • Effect of vegetation on the electrical system • Other effects beside vegetation falling on lines (e.g., line-to-tree faults, etc.) • Additionally the effect of the electrical system on the surrounding community • Model the fire initiation and propagation risk through vegetation • Develop a vulnerability index for the surrounding community • Account for recent history, damage, impact zones, magnitudes • Use US Forest Service models to assist model development for fire behavior

  19. GRIP Technical Team • SLAC National Accelerator Laboratory • PresencePG • Packetized Energy • National Rural Electric Cooperative Association • Lawrence Berkeley National Laboratory

  20. Acknowledgements GRIP Technical Advisory Group Members

  21. Questions?

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