The intermittent and randomness of distributed generations and charging power of electric vehicles bring new challenges to the distribution network reconfiguration. In this context
an interval number method based dynamic reconfiguration strategy considering electric vehicles and distributed generation is proposed. This paper introduces the uncertainty of the distribution power supply and the charging load of electric vehicles. The objective function to minimize network loss by interval number is proposed. Meanwhile
the dynamic time division of dynamic loss parameters is come up with and a mathematical model of dynamic reconfiguration of distribution network is established. The flow equation is solved by using Krawczyk-Moore interval iteration method as well as an affine multiplication and division operation is introduced to replace the interval multiplication
which help to solve the problem of interval arithmetic. Finally
the optimization method for the model above is proposed by combining neighborhood search and clonal selection algorithm
in order to realize dynamic reconfiguration of active distribution network with multiple uncertainties. The analysis of the example shows that the proposed method is superior to the traditional artificial intelligence algorithm.
Analysis on Probabilistic Joint Flow for Transportation Network and Distribution Network Considering Multiple Uncertainties of Road-Vehicle-Source-Load
Improved Affine State Estimation for Distribution Network Considering Uncertainty of Distributed Generator
Real-time Dispatch Strategy for Electric Vehicles Based on Deep Reinforcement Learning
风电爬坡事件多级区间预警方法
需求响应视角下的配电网不确定性重构模型
Related Author
Xiaotao FANG
Zheng YAN
Han WANG
Xiaoyuan XU
Yue CHEN
Shaolun XU
Zhengwei QU
Jiaxi ZHANG
Related Institution
Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education(Shanghai Jiao Tong University)
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong
Key Laboratory of Power Electronics for Energy Conservation and Motor Drive of Hebei Province (Yanshan University)
Maintenance Branch Company of State Grid Jibei Electric Power Co., Ltd.
Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University)