A COST-EFFICIENT APPROACH FOR EV ENERGY MANAGEMENT IN A MICROGRID

Abstract

This thesis introduces a particle swarm optimization (PSO)-based energy management system (EMS) integrated into a microgrid (MG) infrastructure for charging electric vehicles (EV). The primary objective of this study is to enhance the efficiency of integrating locally produced renewable energy, power from the grid, and battery energy storage systems (BESS) to achieve cost-effectiveness and optimize the usage of sustainable resources within a community context. The design of the MG incorporates several components, including wind turbines (WT), solar panels (PV), and EV charging stations. The demand and energy generation profiles are modeled and used as MG inputs for analyzing 16 different scenarios with four seasons. The results demonstrate the versatility and effectiveness of the PSO-based EMS in attaining a financially viable MG system that satisfies the hourly energy demand criteria. This study aims to thoroughly comprehend the influence of the optimized EMS on EV charging schedules and MG performance. The findings of this research provide valuable insights for the development of sustainable and economically feasible EMS in community settings. Overall, the use of PSO in the proposed EMS for EV charging and battery charging/discharging is based on electricity price. Additionally, the results show that the implementation of PSO-based optimization results in a cost reduction of 21% and 16% in winter, 17% and 8% in spring, 12% and 9% in autumn, 21% and 14% in summer with and without EV consideration, respectively. These findings highlight the effectiveness of the PSO optimization technique in efficiently adjusting to various energy demand patterns.