Self-learning PSO based optimal EVs charging power control strategy for frequency stabilization considering frequency deviation and impact on EV owner

Nowadays, electric vehicles (EVs) raise numerous challenges and opportunities in microgrids. The EV deployment is increasingly gaining in importance for the purpose of providing ancillary services, especially grid frequency support. This paper presents a multi-objective optimal EVs charging power control strategy providing frequency stabilization in microgrids. The proposed strategy employs a dynamic droop controller improved by a Takagi-Sugeno (TS) fuzzy logic system, yielding good dynamic response and robustness. In this paper, the proposed strategy particularly allows an EV aggregator to temporarily reduce or pause the EVs charging power under the permissible range. Moreover, a self learning particle swarm optimization (SLPSO) is applied to optimally tune the membership functions in the fuzzy logic system based on the multi-objective optimization problem, minimizing frequency deviation and impact on EV owner in terms of charging power deviation. The proposed strategy is investigated using AC microgrids performed by DIgSILENT Powerfactory simulation software. The simulation study is carried out to verify the performance and effectiveness of the proposed strategy in comparison with some existing control strategies. The simulation results reveal that the proposed strategy offers optimal solutions for frequency stabilization in terms of satisfying the grid utility and EV owner perspectives simultaneously. (c) 2021 Elsevier Ltd. All rights reserved.