Optimized framework for strategic electric vehicle charging station placement and scheduling in distribution systems with renewable energy integration

Abstract

Increased demand for electric vehicles (EVs) is faced with challenges by existing electrical grids. To address these challenges, in this study, a comprehensive framework is developed for the strategic placement of electric vehicle charging stations (EVCSs) in a distribution system and efficient charging and discharging schedules of EVs in the EVCSs. Optimal locations for EVCSs in a distribution system are identified with system inefficiencies and the inclusion of renewable energy sources (RESs), such as solar PV. EV scheduling is performed considering the power exchange between EVCSs and the grid with the integration of RESs in the distribution system. The framework is presented as an optimization problem and is addressed through the particle swarm optimization (PSO) approach. For comparison, the proposed model is also solved using the genetic algorithm (GA) and sine cosine algorithm (SCA). The IEEE 33 bus system is used as a test system to implement the suggested approach. The simulation outcomes show the effectiveness of the proposed model. The proposed PSO-based approach demonstrates significant improvements, reducing power losses by 10.29% for optimal EVCS placement compared to random placement, while also achieving cost reductions of 25.42% and 32% compared to SCA and GA, respectively, through optimized EVCS placement and scheduling. Validation through real-time implementation is performed using the OPAL-RT platform. The experimental setup confirms the real-time feasibility of the suggested approach.