Transfer scheduling strategy for urban high-voltage distribution network with highly-penetrated renewable energy
DOI：10.19783/j.cnki.pspc.190837
Key Words:probability assessment  photovoltaic consumption  local block control  reconfiguration
Author NameAffiliationE-mail
LIU Fangfang College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China  
LÜ Lin College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China  
LIU Youbo* College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China liuyoubo@scu.edu.cn 
HUANG Yang Chengdu Power Supply Company, State Grid Sichuan Electric Power Company, Chengdu 610041, China  
LIU Chang State Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610072, China  
CHEN Gang State Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610072, China  
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Abstract:In order to solve the problem of local consumption and overload given a high penetration of electric vehicles and non-uniform integration of photovoltaic power, a bi-level optimal model is proposed based on a method of network topology risk analysis of multi-dimensional probabilistic estimation. The upper model aims to improve the consumption of photovoltaic output and determine the alternative topology of 110 kV network. In the lower model, the optimization goals are the loading rate of the substation and numbers of overloaded lines. In order to improve the efficiency of the algorithm, the alternative topology of a unit group is defined as variable instead of the traditional coding method. This reduces the solution dimension. The result indicates that the proposed method can solve local overload by coordinating the reconfiguration of the high voltage distribution network and photovoltaic energy system. This ensures the security of an urban gird that has a large scale of clean energy integration. This work is supported by National Natural Science Foundation of China (No. 51807127) and Sichuan Science and Technology Department Project (No. 2019YFG0152).
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