Optimization Operation Strategy of Active Distribution Network Considering Dynamic Network Reconfiguration
Li Chao1, Miao Shihong1, Sheng Wanxing2, Zhang Di1, Hu Shijun3
1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key Laboratory School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan 430074 China; 2. China Electric Power Research Institute Beijing 100192 China; 3. Electric Power Research Institute State Grid Anhui Electric Power Co. Ltd Hefei 230601 China
Abstract The dynamic network reconfiguration (DNR) technology can effectively minimize the overall economic operation cost of the distribution network and its curtailment rate of renewable distributed generation (RDG). In this paper, an active distribution network optimization operation model considering DNR was established. Herein, the lowest comprehensive operation cost of distribution network was taken as the goal, and the dispatchable resources were considered such as DNR switches, RDG and reactive power compensation device. The absorption rate of RDG thus could be effectively improved, while the distribution network operation constraints were satisfied. The model transformation method based on the second-order relaxation and variable-product linearization was proposed. Consequently, the non-convex sources in the constraints were transformed into convexity, and the optimization model was transformed into a mixed integer second-order cone programming (MISOCP) problem. Thus, the complexity of solving process was greatly reduced. Finally, the extended IEEE 33-node distribution network was adopted to conduct simulation calculation. The results demonstrate the validity of the proposed model and its transformation method.
Li Chao,Miao Shihong,Sheng Wanxing等. Optimization Operation Strategy of Active Distribution Network Considering Dynamic Network Reconfiguration[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3909-3919.
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2019, Vol. 34 
