Methodology for Selecting Parameters of Electric Energy Storage Systems in Microgrid 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, due to the complexity of solving multidimensional uncertainty problems in microgrids. Regarding the comprehensive assessment of EES parameters considering the influence of various factors, despite numerous studies dedicated to the evaluation and rational selection of EES parameters, this task 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, EES performance degradation, as well as the reliability and cost of microgrid implementation to ensure uninterrupted operation of EV supply equipment within a distribution network with limited available power capacity. The developed method and EVCS load profile model enable 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 EES, WPP, and EVCS. The EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.