Electric vehicle charging infrastructure planning with integrated energy management and parking behavior analysis

The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.