Abstract
Due to promising potential for environmental sustainability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for charging stations is developed and aims to balance current capital investment costs and future operational revenue. The charging station is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which could be electric battery or recently invented hydro-pneumatic energy storage (GLIDES) system. A co-optimization model that minimizes investment and operation cost is established to determine global optimal solution while combining capacity and operational decision making. EV mobility is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in design space and then fixed in solving the co-optimization model. Sampled parameters include: EV charging slots number, PV area, capacity of energy storage system, and daily mean EV arrival number. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models which could provide insights for charging station placement in different practical situations. The effectiveness of proposed surrogate modeling approach is demonstrated in the numerical experiments.
