Methodology for Selecting Parameters of Electric Energy Storage Systems in Microgrids with Integration of Electric Vehicle Charging Stations

Currently, research aimed at optimizing the power rating and energy capacity of electrical energy storage (EES) systems while accounting for multiple sources of uncertainty remains underrepresented in the scientific literature, primarily due to the complexity of solving multidimensional uncertainty problems in microgrids. With regard to the comprehensive assessment of EES parameters under the influence of various factors, despite numerous studies devoted to the evaluation and rational selection of these parameters, the problem remains largely unresolved. This paper proposes a methodology for selecting EES parameters that accounts for the uncertainty of wind power plant (WPP) generation and electric vehicle charging station (EVCS) load and EES performance degradation, as well as the reliability and cost of microgrid implementation. The goal is to ensure the uninterrupted operation of electric vehicle supply equipment within a distribution network with limited available power capacity. The developed method, together with the EVCS load profile model, enables the generation of a time-based power profile under input data uncertainty. The work presents a mathematical model of microgrid operation that considers the integrated performance of the EES, WPP, and EVCS. The proposed EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.