Locating and sizing method for energy interconnection oriented active distribution networks based on stochastic chance constrained programming
DOI：10.19783/j.cnki.pspc.191118
Key Words:energy interconnection  active distribution network  locating and sizing  stochastic chance constrained programming  artificial fish swarm algorithm
Author NameAffiliation
ZHAO Liping Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075001, China 
ZHANG Shuwei Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075001, China 
ZHANG Xueyan Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075001, China 
LI Yonggang Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075001, China 
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Abstract:As an important part of the energy internet, an energy interconnection-oriented active distribution network has been a research frontier in the field of intelligent distribution networks. In order to solve the problem of locating and sizing of an energy interconnection-oriented active distribution network, a stochastic chance constrained programming model is established considering system uncertainty. Considering the multi-energy complementary coordination characteristics of the system, life cycle cost theory is adopted to take into account the purchase cost of equipment, installation, operation, maintenance and system operation costs and residual value recovery. The comprehensive cost of location and capacity of a distribution network in probability form is taken as the objective function, and necessary constraints such as the capacity of equipment configuration, system power supply reliability, power balance, and equipment operation, etc. are taken into account. It introduces a feedback strategy, a global optimum record, and an adaptive visual field and step size to improve the basic artificial fish swarm algorithm, and then uses the improved algorithm to solve the built model. Finally, the effectiveness of the proposed strategy and the improved algorithm is verified by simulating two kinds of locating and sizing scenarios. This work is supported by Beijing-Tianjin-Hebei Cooperation Special Project of National Natural Science Foundation of Hebei Province (No. F2016203507).
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