A Tri-Level Demand Response Framework for EVCS Flexibility Enhancement in Coupled Power and Transportation Networks

被引:10
作者
Qian, Tao [1 ,2 ,3 ]
Liang, Zeyu [1 ]
Chen, Sheng [4 ]
Hu, Qinran [1 ,2 ,3 ]
Wu, Zaijun [1 ]
机构
[1] Southeast Univ, Sch Elect Engn, Nanjing 210000, Peoples R China
[2] Southeast Univ, Jiangsu Key Lab Smart Grid Technol andEquipment, Nanjing 210000, Peoples R China
[3] Minist Transport, Key Lab Transport Ind Comprehens TransportationThe, Nanjing Modern Multimodal Transportat Lab, Beijing, Peoples R China
[4] Hohai Univ, Coll Energy & Elect Engn, Nanjing 210098, Peoples R China
基金
中国国家自然科学基金;
关键词
Load modeling; Pricing; Transportation; Costs; Uncertainty; Optimization; Electricity supply industry; Electric vehicles charging station; coupled power and transportation network; demand response; deep reinforcement learning; ELECTRIC VEHICLES; STATIONS; EQUILIBRIUM; OPERATIONS; STRATEGY; MODEL; FLOW;
D O I
10.1109/TSG.2024.3417294
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:598 / 611
页数:14
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