A Novel Multiobjective Optimization Approach for EV Charging and Vehicle-to-Grid Scheduling Strategy

In this study, we proposed a novel multiobjective optimization technique for electric vehicles’ (EVs) charging and vehicle-to-grid (V2G) scheduling. The ring seal search (RSS) algorithm ensures the optimum compatibility of the EV charging and discharging profiles revolving around multiple objectives, such as cost of charging, peak load demand reduction, and grid stability. The proposed algorithm is tested on the distribution model through the IEEE 33-bus system. A comprehensive model with real-time data from EV charging station operators (CSOs) is also ported in this research so that the EVs can be illustrated in the power distribution network. A convenient energy management strategy (EMS) called multiobjective optimization has been introduced to provide practical solutions for CSOs and EV users. The strategy had been developed based on multiple objectives to counter different trade-offs, including EV charging and discharging profiles suitable for numerous objectives encompassing charging costs, peak load demand reduction, and grid stability. The efficiency of the deterministic approach has been verified via extensive simulations and analysis, and the outcomes incorporate a definite enhancement in metrics since the RSS algorithm considerably optimized EV charging and V2G scheduling. The EV charging and discharging profiles had been optimized to take better advantage of available resource requirements by accommodating priority-based scheduling. The RSS will be able to investigate the modified parameter and the modified real system, which tells about the versatility and adaptability of the proposed method, i.e., it will be able to incorporate the changing implementation and its real-world efficacy, which is an immense merit for the adaptation of the real system. The proposed method offers a comprehensive, advanced EV charging and V2G scheduling solution. It addresses the limitations of previous methods by considering multiple objectives, utilizing a novel optimization algorithm, handling uncertainties, promoting renewable energy integration, and providing ancillary grid support. These enhancements make our method more effective, flexible, and capable of supporting the transition to a sustainable and efficient energy system.