Minimizing Grid Dependency and EV Charging Costs with PSO-Based Microgrid Energy Management

Energy management in grid-connected systems Micro-grids (MG) has evolved rapidly in recent years as a result of environmental concerns, rising energy consumption, and the market liberalization of electricity merchandising. The Energy Management System (EMS) optimizes the utilization of MG's energy resources and power storage facilities in the supply-demand prices at the lowest cost possible. The purpose is to develop a Particle Swarm Optimization (PSO) EMS in MG, which assists in minimizing the dependence on the grid and decreasing the cost of Electric Vehicle (EV) charging by utilizing Renewable Energy Resources (RES) which comprises power production and power purchase from the electricity utility. An MG model for all over the distribution network contains photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and various EVs, the load of which also varies. This paper analyzes the following operating conditions: the EV demand, the load and production variations, and the grid power costs to determine the effectiveness of an EMS that coordinates power from multiple sources. The study also supports the efficacy of the suggested EMS, as it proves that these systems contribute to the efficient EMS transactions with the grid, under the variety of pricing models and the integration of RES. In addition, the findings show that the integrated solution of the proposed PSO-based optimization decreases the costs by 8%. This recommends a scheduling strategy that lowers EV energy use and has a BESS charging /discharging management system during peak and off-peak hours. Also, the provision of EV charging is an effective way of cutting costs while at the same time preserving energy.