Optimization-Based Energy Management for Grid-Connected Photovoltaic–Battery Systems in Smart Grids Using Demand Response and Particle Swarm Optimization

Abstract

With rising temperatures and increasing power demands, microgrid failures have become frequent, highlighting the need for effective energy management. Microgrids, particularly those integrating renewable energy sources (RES), are gaining traction as decentralized energy solutions. Despite their potential, Photovoltaic (PV) systems face challenges due to the intermittent nature of solar energy, necessitating energy storage solutions to maintain a stable power supply. Battery energy storage systems (BESS) are critical in buffering power fluctuations and enhancing grid stability, forming PV-battery hybrid microgrids capable of operating in both grid-connected and islanded modes. This study focuses on optimizing the management of BESS within a solar-integrated microgrid over 24 h to improve energy efficiency and cost-effectiveness. Additionally, the study examines the implementation of demand response (DR) techniques, including peak clipping, valley filling, and load shifting, to further enhance grid stability and economic benefits. Using MATLAB for simulations, the study employs state flow study and linear programming methods. Results indicate that the energy management system (EMS) using particle swarm optimization (PSO) enhances the efficiency of EMS using linear programming (LP). Simulation results conducted using MATLAB R2023b indicate that PSO outperforms LP in minimizing daily electricity costs (up to 15.32% savings), stabilizing state of charge (SoC), and reducing grid power fluctuations. These findings underscore the importance of advanced EMS in enhancing microgrid efficiency, particularly under variable weather conditions. This research underscores the crucial role of energy management systems (EMS) in enhancing the reliability and sustainability of microgrids, particularly in rural and underdeveloped areas. By optimizing the charge and discharge cycles of BESS based on load requirements and implementing DR strategies, the proposed methods demonstrate substantial improvements in system performance and economic benefits.