| 引用本文: | 杨磊,杨晓辉,吴越,等.基于改进猫群算法的分布式电源优化配置[J].电力系统保护与控制,2019,47(1):95-100. |
| YANG Lei,YANG Xiaohui,WU Yue,et al.Research on optimized distributed generations locating based on modified cat swarm optimization[J].Power System Protection and Control,2019,47(1):95-100 |
|
| 摘要: |
| 对分布式电源(DG)进行合理的选址和定容能使其经济效益最大化。目前用于解决分布式电源优化配置的大多算法都存在对控制参数依赖性过强的问题，导致算法容易陷入局部最优解。为解决该问题，提出了一种混沌改进的多目标猫群算法。利用混沌理论的随机性、遍历性及其规律性，对猫群算法的参数进行调整，使算法能快速得出全局最优解。在分析DG特性的基础上，建立了考虑含分布式电源的有功网损费用最小和用户购电成本最小模型。最后，以PG&E69节点配电网为例，通过将改进算法与粒子群算法及基本猫群算法的效果对比，验证改进算法对分布式电源优化配置问题的有效性。 |
| 关键词: 分布式电源 改进猫群算法 多目标优化 混沌理论 配电网 |
| DOI：10.7667/PSPC171900 |
| 分类号: |
| 基金项目:国家863课题资助(2013AA013804);国家自然科学基金项目资助(51765042，61463031，61662044);江西省科技支撑计划项目资助(20142BBE50037，20151BBE50050) |
|
| Research on optimized distributed generations locating based on modified cat swarm optimization |
|
YANG Lei1,YANG Xiaohui1,WU Yue2,WEN Hechang1,ZHU Yunfeng1
|
|
(1. School of Information Engineering, Nanchang University, Nanchang 330031, China;2. State Grid Jilin Electric Power Company, Changchun 130000, China)
|
| 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). |
| Key words: distributed generation modified cat swarm algorithm multi-objective optimization chaos theory distribution network |
