Resilient Microgrid Scheduling With Synthetic Inertia From Electric Vehicles Within a Network of Charging Stations

Abstract

Vehicle-to-grid technologies are proposed as potential providers of virtual inertia for microgrids (MGs). This article addresses an energy and charging scheduling problem for an MG and investigates how to utilize a network of electric vehicle (EV) charging stations (CSs) to provide sufficient virtual initial for frequency regulation that guarantees the safe transition of MG to the islanded operation during extreme events. The charging behavior of EV within a CS network is complex and can be actively influenced by charge point power and tariff set up by the CS network operator subject to MG operation requirements. A novel modeling framework is proposed to capture these aspects and integrate them into the MG energy management. The goal is to determine the optimal power allocation among distributed energy resources within an MG, minimizing operation costs while ensuring sufficient frequency support with virtual inertia contribution from EVs. To deal with inevitable uncertainties associated with EV arrivals at a CS, we employ joint distributionally robust chance constraints (DRCCs) to mitigate the impact of uncertainty and enhance the robustness of the algorithm. These joint DRCCs are decomposed into individual ones via an optimized Bonferroni approximation (BoA) method, then suitably relaxed into convex forms, which maintains the solvability of the overall problem. The effectiveness of the method is validated with case studies based on a modified IEEE 14-bus system.