Stochastic behavior modeling and optimal bidirectional charging station deployment in EV energy network

Abstract

Electric vehicle energy network (EVEN) enables the transmission of renewable energy from rural to urban area by the flexibility of EVs via energy exchange. In this paper, (dis)charging behavior modelling and bidirectional charging station (BCS) deployment optimization are addressed, since they are crucial in EVEN for EV accommodation, renewable energy utilization, drivers’ profitability estimation, operators’ cost assessment, and financial policy establishment. A novel stochastic Markov (dis)charging behavior model is proposed to calculate the spatiotemporal load pattern considering the realistic factors such as personal features, state of charge (SoC), electricity price, and BCS locations. Unlike most works ignoring energy trading, six scenarios are explored: (S1) no trade; (S2) trade in main battery. (S3) trade in extra battery. (S4) trade in extra ultracapacitor; (S5) trade in both main and extra battery; (S6) trade in both main battery and ultracapacitor. Also, a multi-objective BCS deployment strategy is newly designed, aiming at minimizing installation cost and driver’s electricity bill, while quality of service (QoS) and voltage stability are ensured. An improved hybrid algorithm is developed, which combines hill climbing for enhanced exploitation and particle swarm optimization for better evolvement based on genetic algorithm framework. The simulation validates the fitting ability of charging model, the effectiveness of parameter selection algorithm and the deployment approach. Comparing 6 scenarios, benefits of energy trading in EVEN is confirmed and the superiority of ultracapacitor for trading is demonstrated. The feasibility of financial policies is also studied, and certain guidance is provided for drivers to improve their cost.