Optimal capacity determination of photovoltaic and energy storage systems for electric vehicle charging stations

With the growing interest in integrating photovoltaic (PV) systems and energy storage systems (ESSs) into electric vehicle (EV) charging stations (ECSs), extensive research has focused on methods to increase the profits of ECS operators (ECSOs). Conventional studies have primarily relied on empirical methods, such as assuming a constant value for the power conversion system (PCS) capacity or modeling it as being dependent on the battery capacity, to design ESSs. However, such empirical methods can lead to suboptimal or excessive determinations of the capacity of a facility. This study proposes a battery-independent PCS model that independently models the battery and PCS capacities in ESS design to overcome the limitations of the conventional model and maximize the profit for ECSOs. The proposed model determines the optimal capacity of ESS and PV to maximize ECSO's profit. The nonlinearities that arise from using a battery-independent PCS model are linearized by the BIG-M method to effectively solve the optimization problem. The proposed model achieved an additional profit of up to 1.5 % compared to the conventional model. Additionally, the proposed model was simulated under various conditions (e.g., decreasing capital investment costs of ESS, changing EV charging demand, changing time-of-use rates, and applying real-time price rates). Accordingly, it is confirmed that the proposed model greatly contributes to improving ECSO profit even under various conditions. Moreover, the proposed model offers a means to determine the optimal capacities of PV and ESS in an ECS, ultimately maximizing ECSO profits. • Electric vehicle charging station operators' profitability increased. • Economic evaluation and optimal capacity determination of PV-ESS performed. • Battery-independent PCS modeling adopted to design ESS. • BIG-M method used to linearize the non-linear problem to solve optimization problem. • Various factors affecting PV and ESS capacities and operator profit are analyzed.