A systematic review on dispatch strategies in microgrids with electric vehicle integration: economic, environmental, and power quality perspectives

Abstract

This study presents a systematic literature review of optimization approaches applied to energy management problems in EV-integrated microgrids. The optimization techniques are classified into three categories: classical, heuristic, and hybrid approaches. The analysis evaluates the effectiveness of each category based on their ability to improve power quality, enable cost-effective energy dispatch, and reduce carbon emissions. Results show that heuristic optimization techniques dominate this research area, accounting for 51.6%, with particle swarm optimization and genetic algorithms being the most commonly employed due to their practical versatility and adaptability. Classical techniques represent 22.5% of the approaches reviewed but exhibit limitations in solving highly complex and large-scale optimization problems due to restricted scalability and solving nonlinear system complexities. Hybrid optimization techniques account for 25.8% and demonstrate superior performance by leveraging the combined strengths of heuristic and classical methods. Hybrid approaches exhibit better convergence rates, higher solution accuracy, and improved scalability compared to standalone classical methods, making them particularly effective for complex, multi-objective optimization problems in EV-integrated microgrids.