Distributed Dynamic Tariff for Congestion Management in Distribution Networks Considering Temporal-Spatial Coordination of Electric Vehicles

• Published in: IEEE transactions on transportation electrification 2024-09, Vol.10 (3), p.7358-7373
• Main Authors: Shen, Feifan, Lin, Siyao, Wei, Juan, Huang, Sheng, Wu, Qiuwei, Shen, Yangwu, Zhu, Lipeng, Wang, Pengda, Wang, Bozhong
• Format: Article
• Language: English
• Subjects: Congestion; Congestion management; Coordination; distributed optimization; Distribution networks; dynamic tariff (DT); electric vehicle (EV); Electric vehicle charging; Electric vehicles; Indexes; Information management; Optimization; Partial discharges; Power system dynamics; Spatial distribution; Tariffs; Transportation; Transportation networks; Travel time; Vehicle dynamics
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2023.3332892

A distributed dynamic tariff (DDT) method was proposed in this study for congestion management in distribution networks considering the temporal-spatial coordination of electric vehicles (EVs). First, a quadratic DT-based congestion management model was formulated considering the full travel route as well as the charging and discharging (C&D) behavior of EVs in the transportation network (TN). Second, a distributed congestion management framework based on the asynchronous distributed alternating direction method of multipliers (AD-ADMM) was introduced. Using the proposed framework, DDTs were iteratively optimized between the distribution system operator (DSO) and aggregators to achieve optimal EV routing in the TN and congestion management in the distribution network. Finally, case studies were conducted on two test systems, and the numerical results showed that the proposed DDT method can optimize the temporal-spatial distribution of EVs in the TN, minimize the travel time and C&D costs of EVs in each aggregator, and effectively avoid congestion in the distribution network. Moreover, the optimization efficiency and privacy information protection of aggregators in the DT-based congestion management method could be improved.