A Tri-Level Demand Response Framework for EVCS Flexibility Enhancement in Coupled Power and Transportation Networks
Tao Qian, Zeyu Liang, Sheng Chen, Qinran Hu, Zaijun Wu
Abstract
The increasing penetration of electric vehicles (EVs) presents an opportunity for demand response (DR) programs in the power network to improve overall operation efficiency. However, in the transportation network (TN), the potential of EV charging stations (EVCS) participating in DR needs to be more effectively harnessed. This paper proposes a tri-level DR framework to improve the EVCS flexibility via power distribution network (PDN) designing an incentive mechanism for EVCS in the coupled power and transportation network (CPTN). The proposed framework encompasses the distribution network operators (DNOs), EVCS and EVs, while ensuring information security and independent operation within each level. This framework integrates heterogeneous strategies of different participants aimed at profits maximization or operation safety, towards a dynamic equilibrium. A deep reinforcement learning-based (DRL) framework is proposed to obtain the DR strategies in the tri-level framework. The dynamic equilibrium between EVCS and DNOs is approximated through iterative training of safe DRL. The effectiveness of the proposed tri-level framework is validated through a real-world test system from Nanjing City, China, consisting of 39 nodes and 10 EVCSs. Numerical results confirm that the proposed DR program enhances the operational flexibility of EVCSs compared to traditional charging load regulation methods.