Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study

Abstract

The increasing market share of electric vehicles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are guaranteed. In this study, an economical and technical strategy is developed. It focuses on finding proper EVCSs by EVOs and determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met simultaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by bilevel programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, a case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in San Francisco for charging/discharging 247 EVs during 24 hours of a typical day. Simulation results show that under implementing the proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.