1. 电力传输与功率变换控制教育部重点实验室(上海交通大学), 上海市 200240; 2. 上海交通大学电子信息与电气工程学院, 上海市 200240; 3. 中国电力科学研究院有限公司, 北京市 100192
1. Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai 200240, China; 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3. China Electric Power Research Institute, Beijing 100192, China
In order to deal with the problem of multi-level coordinated operation of distribution network with high penetration of renewable energy, a fuzzy chance constrained source-grid-load coordinated optimal operation method is proposed based on the multi-agent system and multi-level electricity price response mechanism. According to the operating characteristics of distributed generators, active loads and microgrids, this paper analyzes the existing problems in the coordinated source-grid-load operation of distribution networks with high penetration of renewable energy. The coordinated optimization architecture is designed based on the multi-agent system and multi-level electricity price response mechanism. Considering the uncertain factors of renewable energy and active loads, and the risk difference of multi-agents, the coordinated interaction between the active distribution network and the source, load, and microgrid is described by the method of fuzzy chance constrained programming. The optimization models at the distribution network layer, directly coordinated source-load layer and indirectly coordinated microgrid layer are developed. The neuronal network and the equivalence class transformation are introduced to deal with the uncertain factors in the stochastic programming model. The bat algorithm and golden section method are used to solve the problem. Finally, the simulation results show the effectiveness of the proposed method.
XU Xilin, SONG Yiqun, YAO Liangzhong,et al.Source-Grid-Load Coordination Method for Active Distribution Network Based on Multi-level Electricity Price Response Mechanism[J].Automation of Electric Power Systems,2018,42(5):9-17. DOI:10.7500/AEPS20170430002.