A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

• Published in: IEEE transactions on industry applications Vol. 60; no. 4; pp. 5317 - 5330
• Main Authors: Xue, Lin, Niu, Tao, Ge, Huaichang, Zhang, Jiahui, Xue, Yixun, Fang, Sidun, Chen, Guanhong, Wang, Zhirou
• Format: Journal Article
• Language: English
• Published: IEEE 01.07.2024
• Subjects: Community distribution network; Costs; Distribution networks; emergency energy storage vehicle (EESV); joint distributed optimization; Optimization; Partitioning algorithms; Routing; Scheduling; Voltage control
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2024.3384474

To address the voltage violation problem caused by large numbers of electric vehicles (EVs) accessing community distribution networks, as well as the large investments in conventional energy storage and difficulties in EV scheduling, this paper proposes a joint distributed optimization framework for voltage control and emergency energy storage vehicle (EESV) scheduling in community distribution networks. First, a Louvain adaptive partitioning algorithm considering regional coupling and equilibrium is employed to divide the network into several independent subareas and reduce the scale of the original problem. Then, an EESV transportation routing model and the distribution network voltage/Var control are co-optimized based on the network partition result, which aims to minimize the actual power loss, voltage deviation degree, and EESV operation cost. Moreover, an improved ADMM algorithm with variable penalty parameters is used to improve the solution efficiency. Finally, the results of two modified distribution network systems demonstrate that the proposed framework can effectively reduce the network loss and voltage deviation compared with other methods in the literature and guarantee fast convergence.