Load Restoration in Islanded Microgrids: Formulation and Solution Strategies

TL;DR

This paper addresses load restoration in islanded microgrids using a non-convex optimization framework, proposing convex relaxation and policy-learning methods to effectively restore power after disruptions.

Contribution

It introduces a comprehensive non-convex formulation for load restoration in islanded microgrids and develops novel solution strategies including convex relaxation and constrained policy optimization.

Findings

Convex relaxation provides a computationally efficient baseline solution.

Policy-learning methods outperform traditional optimization in complex scenarios.

Deep learning approaches show promising results in restoring microgrid operations.

Abstract

Adverse circumstances such as extreme weather events can cause significant disruptions to normal operation of electric distribution systems (DS), which includes isolating parts of the DS due to damaged transmission equipment. In this paper, we consider the problem of load restoration in a microgrid (MG) that is islanded from the upstream DS. The MG contains sources of distributed generation such as microturbines and renewable energy sources, as well as energy storage systems (ESS). We formulate the load restoration task as a non-convex optimization problem. This problem embodies the physics of the MG by leveraging a branch flow model, while incorporating salient phenomenon in islanded MGs such as the need for internal frequency regulation, and complementarity requirements arising in ESS operations. Since the formulated optimization problem is non-convex, we introduce a convex relaxation which can be solved through model predictive control as a baseline method. However, in order to solve the problem considering its full non-convexity, we leverage a policy-learning method called constrained policy optimization, a tailored version of which is used as our proposed algorithm. The aforementioned approaches, along with an additional deep learning method are compared through extensive simulations.