Research on optimized distributed generations locating based on modified cat swarm optimization
DOI：10.7667/PSPC171900
Key Words:distributed generation  modified cat swarm algorithm  multi-objective optimization  chaos theory  distribution network
Author NameAffiliation
YANG Lei School of Information Engineering, Nanchang University, Nanchang 330031, China 
YANG Xiaohui School of Information Engineering, Nanchang University, Nanchang 330031, China 
WU Yue State Grid Jilin Electric Power Company, Changchun 130000, China 
WEN Hechang School of Information Engineering, Nanchang University, Nanchang 330031, China 
ZHU Yunfeng School of Information Engineering, Nanchang University, Nanchang 330031, China 
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Abstract:The rational location and capacity of Distributed Generations (DG) can maximize its economic benefit. Most of the algorithms which are used to solve the optimal configuration of distributed power supply have the problem of too strong dependence on the control parameters, which are easy to fall into the local optimal solutions. To solve the problem, this paper proposes a chaos improved cat swarm optimization algorithm. The parameters of cat swarm optimization algorithm can be adjusted by using the randomness, ergodicity and regularity of the chaos theory, which makes the algorithm get the global optimal solution quickly. Based on the detailed analysis on the characteristics of DG, a model is built to minimize active power loss cost and consumer electricity purchase cost. Finally, taking PG&E69-node distribution network as an example, this paper compares the effect of this improved algorithm with the PSO algorithm and the basic cat group algorithm to verify the effectiveness of modified cat swarm optimization algorithm to locate DG optimally. This work is supported by National High-tech R & D Program of China (No. 2013AA013804), National Natural Science Foundation of China (No. 51765042, No. 61463031, and No. 61662044), and Science and Technology Support Plan of Jiangxi Province (No. 20142BBE50037 and No. 20151BBE50050).
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