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

TL;DR

This paper introduces a policy-mode framework combined with distributionally robust optimization to enhance microgrid resilience against unpredictable cyber-physical threats, effectively handling sparse historical data and diverse event types.

Contribution

It proposes a novel policy-mode framework integrated with DRO to improve microgrid resilience under uncertain and sparse data conditions.

Findings

Enhanced microgrid resilience demonstrated on IEEE 123-node feeder

Framework effectively manages data sparsity and event unpredictability

Improved operational robustness under cyber-physical threats

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.