A Hybrid Multi-Criteria Decision-Making and Multi-Objective Framework for Optimal Sizing of PV-powered EV charging station with Battery Storage

The fast-paced expansion of Electric Vehicles (EVs) necessitates the establishment of efficient, sustainable, and resilient infrastructure for charging. The design of EV charging stations (EVCS) should incorporate renewable energy sources to reduce grid stress and adopt a multifaceted approach that considers economic, social, reliability, and environmental parameters. However, existing optimization studies primarily focus on economic and reliability criteria and often tend to neglect social and environmental considerations, leading to suboptimal and inequitable solutions. This study proposes a hybrid optimization and decision-making framework to determine the optimal configuration of a PV-powered EVCS having battery backup simultaneously considering all 4 parameters- economic, social, reliability, and environmental factors through a two-layer approach. A Multi-Objective Particle Swarm Optimization (MOPSO) approach is employed to identify optimal configurations in the first layer, which are then evaluated using a hybrid TOPSIS-AHP-based Multi-Criteria Decision-Making (MCDM) method to select the most balanced solution in the second layer. The proposed framework was implemented usingMATLAB, which enabled efficient development and testing of the algorithm. Three case studies were conducted to evaluate the influence of Analytic Hierarchy Process (AHP) derived weights on the system design obtained using the proposed framework. In Case Study 1, where greater emphasis was placed on economic performance, the resulting configuration featured high PV capacity, low battery storage, and minimal grid dependence ensuring cost-effectiveness but introducing potential intermittency risks. Case Study 2, which prioritized reliability, yielded a design with both high PV and battery capacities and reduced grid reliance, thereby enhancing system resilience at the expense of higher capital investment. Case Study 3 adopted a balanced weighting approach, resulting in a configuration that moderately satisfied all performance criteria. These findings underscore the pivotal role of weight selection in multi-criteria optimization and emphasize the need for a balanced strategy that considers economic, environmental, reliability, and social parameters, thereby enabling EV charging station operators to tailor system design to their specific operational priorities.