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Improved microgrid resiliency through distributionally robust optimization under a policy-mode framework

Nawaf Nazir, Thiagarajan Ramachandaran, Soumya Kundu, Veronica Adetola

Abstract

Critical energy infrastructure are constantly understress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. Such events are however hard to predict and numerous in nature and type, making it infeasible to become resilient to all possible cyber-physical event as such an approach would make the system operation overly conservative. Furthermore, distributions of such events are hard to predict and historical data available on such events is sparse. To deal with these issues, we present a policy-mode framework that enumerates and predicts the probability of various cyber-physical events on top of a distributionally robust optimization (DRO) that is robust to the sparsity of the available historical data. The proposed algorithm is illustrated on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources (DERs) and energy storage.

Improved microgrid resiliency through distributionally robust optimization under a policy-mode framework

Abstract

Critical energy infrastructure are constantly understress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. Such events are however hard to predict and numerous in nature and type, making it infeasible to become resilient to all possible cyber-physical event as such an approach would make the system operation overly conservative. Furthermore, distributions of such events are hard to predict and historical data available on such events is sparse. To deal with these issues, we present a policy-mode framework that enumerates and predicts the probability of various cyber-physical events on top of a distributionally robust optimization (DRO) that is robust to the sparsity of the available historical data. The proposed algorithm is illustrated on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources (DERs) and energy storage.
Paper Structure (27 sections, 50 equations, 14 figures, 1 table)

This paper contains 27 sections, 50 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Technical Approach and Accomplishments
  3. Report Structure
  4. Task 1 Single Microgrid Baseline Control
  5. Nomenclature
  6. Optimization Formulation
  7. Linear power-flow model
  8. Baseline simulation results
  9. Task 2 Robust Resilience Verification
  10. Real-time reserve control
  11. Robust optimization verification results
  12. Robust Resiliency Verification via Dual-Based Approach
  13. Uncertainty Set Characterization - Inner approximations
  14. Cyber Attack Scenarios
  15. Chance constrained optimization
  16. ...and 12 more sections

Figures (14)

  • Figure 1: Coupling of SOCP with NLP by fixing real power solutions from SOCP and hence decoupling the NLP to obtain a feasible solution.
  • Figure 2: Available reactive power variation range for NLP across multiple time steps based on the active power trajectory provided by the SOCP.
  • Figure 3: Aggregate generation and load.
  • Figure 4: Case HSLL: (a) Solar curtailment (b) Load curtailment
  • Figure 5: Case HSLL: (a) Nodal voltages (b) Aggregate SoC
  • ...and 9 more figures

Theorems & Definitions (1)

  • Remark 1