Optimizing the load curve through V2G technology for sustainable energy management

This article investigates the potential of Electric Vehicles (EVs) to optimize load curves through Demand Side Management (DSM) by utilizing bidirectional charging capabilities enabled by Vehicle-to-Grid (V2G) technology. The primary objective is to decrease peak demand, reduce load curve fluctuations, and improve grid stability by strategically managing diversified EV charging and discharging landscapes based on their respective State-of-Charge. The simulation results show that utilizing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for DSM reduces the peak and base load variation by 43.52 % and 62.75 % and maximum load fluctuation by 38.85 % and 42.67 %, respectively, for bidirectional charging compared to unidirectional charging. Furthermore, our proposed EV-based DSM strategy was contrasted with the prevailing case that considers the existing vehicles adopting V2G technology. In our proposed illustration using GA and PSO, the reduction percentage for the peak and base load difference becomes 41.06 % and 2.91 % and the maximum load fluctuation reduces to around 48.28 % and 7.13 %, respectively, compared to the current scenario. The seasonal load variations have also been simulated for our proposed strategy to validate the work. The analysis suggests that the widespread use of EVs in demand response strategies could lead to a sustainable energy future in Bangladesh and globally. • Electric vehicles with V2G technology have been utilized to optimize the load curve. • Simulation results evaluate the viability of integrating EVs into the grid. • Results show a notable reduction in load variation for bidirectional charging compared to unidirectional charging. • The proposed case demonstrates better outcomes for effective demand-side management than the existing instance. • Comparative analysis between the GA and PSO algorithms in fulfilling the optimization objectives.