基于猫鼠种群算法的分散式风力发电优化配置

doi:10.11930.2015.6.1

中国电力 ›› 2015, Vol. 48 ›› Issue (6): 1-7.DOI: 10.11930.2015.6.1

• 风光储专栏 • 下一篇

基于猫鼠种群算法的分散式风力发电优化配置

杨珺¹，张闯²，黄旭³，孙秋野¹

1. 东北大学信息科学与工程学院，辽宁沈阳 110819；
2. 江苏省电力公司电力经济技术研究院，江苏南京 210000；
3. 辽宁省电力有限公司电力科学研究院，辽宁沈阳 110006

收稿日期:2015-03-28 出版日期:2015-06-25 发布日期:2015-11-26
作者简介:杨珺（1976—），男，辽宁沈阳人，副教授，主要从事新能源发电及并网优化，智能控制与系统预测等方面的研究。E-mail: yangjun@mail.neu.edu.cn
基金资助:
This work is supported by National Natural Science Foundation of China(61104099,61374124); Fundamental Research Funds for the Central Universities (N130404008,N130104001); Science and Technology Project of State Grid Corporation of China (DKYKJ [2012]001-2).

Optimal Configuration for Distributed Wind Generation Based on Cat and Mouse Swarm Algorithm

YANG Jun¹, ZHANG Chuang², HUANG Xu³, SUN Qiuye¹

1.School of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. Jiangsu Electric Power Company Economic Research Institute, Nanjing 210000, China,
3. Electric Power Research Institute of Liaoning Electric Power Company Limited, Shenyang 110006, China

Received:2015-03-28 Online:2015-06-25 Published:2015-11-26
Contact: 国家自然科学基金资助项目（61104099，61374124），中央高校基本科研业务费专项基金（N130404008，N130104001），国家电网公司科技资助项目（DKYKJ[2012]001-2）

摘要/Abstract

摘要： 发展智能电网是能源可持续发展的重要支撑，而以分散式发电的方式利用可再生能源发电是智能电网的一大特点。从经济、技术和环境等因素出发，将人工鱼群算法和猫群算法相结合，提出了一种新型的优化方法——猫鼠种群算法。研究了分散式风力发电的优化配置问题，包括选择分散式风力发电机的位置和确定风力发电机的容量。猫鼠种群算法同鱼群算法一样，具有参数不敏感性，解决了优化算法参数较多带来的困扰。最后本文以IEEE14节点系统为例，验证了算法的有效性和优越性。

关键词: 分散式发电, 风力发电, 优化配置, 猫鼠种群算法

Abstract: Smart grid is an important basis for energy sustainable development. One of important characteristic of smart grid is distributed renewable power generation. With consideration of economy, technology and environment factors, a new optimal algorithm, cat and mouse swarm algorithm, is presented based on flocking algorithm and cat swarm algorithm. Optimal configuration of distributed wind power generation is studied by using proposed algorithm, including wind turbine location and capacity selection. The proposed algorithm is insensitive to parameters, which is robust during optimization. Simulation results based on IEEE14 node system show the validity and superiority of the proposed algorithm.

Key words: dispersed generation, wind generation, optimal configuration, cat and mouse swarm algorithm

中图分类号:

TM614

杨珺，张闯，黄旭，孙秋野. 基于猫鼠种群算法的分散式风力发电优化配置[J]. 中国电力, 2015, 48(6): 1-7.

YANG Jun, ZHANG Chuang, HUANG Xu, SUN Qiuye. Optimal Configuration for Distributed Wind Generation Based on Cat and Mouse Swarm Algorithm[J]. Electric Power, 2015, 48(6): 1-7.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930.2015.6.1

https://www.electricpower.com.cn/CN/Y2015/V48/I6/1

参考文献

[1] ABDI S, AFSHAR K. Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing[J]. International Journal of Electrical Power & Energy Systems, 2013, 44(1): 786-797.
[2] SOUZAA R d, FERNANDESA A R, Aokia, et al. Sensitivity analysis to connect distributed generation[J]. International Journal of Electrical Power & Energy Systems, 2013, 46, 145-152.
[3] KHATTAM W E, BHATTACHARYA K, HEGAZY Y, et al. Optimal investment planning for distributed generation in a competitive electricity market[J]. IEEE Transactions on Power Systems, 2004, 19(3): 1674-1684.
[4] ACHARYA N, MAHAT P, MITHULANANTHAN N. An analytical approach for DG allocation in primary distribution network[J]. International Journal of Electrical Power and Energy Systems, 2006, 28(10): 669-678.
[5] HUNG D Q, MITHULANATHAN, BANSAL R C. Analytical expressions for DG allocation in primary distribution networks[J]. IEEE Transactions on Energy Conversion, 2010, 25(3): 814-820.
[6] NARA K, HAYASHI Y, IKEDA K, et al. Application of tabu search to optimal placement of distributed generators[C]. Proc IEEE power engineering society winter meeting, 2001, 2: 918-923.
[7] 杨珺，曾昭强，孙秋野，等. 一种基于模糊层次分析法的分散式风电场选址方法[J]. 中国电力，2015，48（4）：151-155.
YANG Jun, ZENG Zhaoqiang, SUN Qiuye, et al. A method of distributed wind farms siting based on fuzzy analytic hierarchy process [J]. Electric Power, 2015, 48(4): 151-155.
[8] GOLSHAN M E H, AREFIFAR S A. Distributed generation, reactive sources and network configuration planning for power and energy-loss reduction[C]. IEEE Proceedings Generation, Transmission and Distribution, 2006: 127-136.
[9] BORGES C L T, FALCAO D M. Optimal distributed generation allocation for reliability, losses and voltage improvement[J]. Electric Power Energy System, 2006, 28(4): 13-20.
[10] 杨珺，张闯，黄旭，等. 基于模拟退火遗传算法的分散式风电选址定容[J]. 东北电力技术，2014，35（2）：6－11．
YANG Jun, ZHANG Chuang, ZHANG Huaguang, et al. Siting and sizing of distributed wind generation based on simulated annealing genetic [J]. Northeast Electric Power Technology, 2014, 35(2): 6-11.
[11] GANDOMKAR M, VAKILIAN M, EHSAN M A. Genetic-based tabu search algorithm for optimal DG allocation in distribution networks[J]. Electric Power Components and Systems, 2007, 33(12): 1351-1362.
[12] 孙秋野，陈会敏，杨家农，等. 牛顿类潮流计算方法的收敛性分析[J]. 中国电机工程学报，2014，34（13）：2196－2200．
SUN Qiuye, CHEN Huimin, YANG Jianong, et al. Analysis on convergence of newton-like power flow algorithm[J]. Proceedings of the CSEE, 2014, 34(13): 2196-2200.
[13] JASMON G B, LEE L. Stability of load flow techniques for distribution system voltage stability analysis[C]// IEEE Proceedings C. Generation, Transmission and Distribution． IET Digital Library, 1991, 138(6): 479-484．
[14] GANDOMKAR M, VAKILIAN M, EHSAN M A． Genetic-based tabu search algorithm for optimal DG allocation in distribution networks[J]. Electric Power Components and Systems, 2007, 33(12): 1351-1362.
[15] 刘健，毕鹏翔，董海鹏．复杂配电网简化分析与优化[M]．北京：中国电力出版社，2002.
[16] 张节潭，程浩忠，姚良忠，等. 分布式风电源选址定容规划研究[J]. 中国电机工程学报，2009，29（16）：1－7．
ZHANG Jietan, CHENG Haozhong, YAO Liangzhong, et al. Study on siting and sizing of distributed wind generation [J]. Proceedings of the CSEE, 2009, 29(16): 1-7.
[17] 李晓磊. 一种新型的智能优化方法-人工鱼群算法[D]. 杭州：浙江大学，2003.

基于猫鼠种群算法的分散式风力发电优化配置

Optimal Configuration for Distributed Wind Generation Based on Cat and Mouse Swarm Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	许光, 匡军, 宋红艳, 张泽虎, 臧祥宇, 张念上, 张玉敏. 面向低压配电台区拓扑结构采集的馈线终端优化配置方法[J]. 中国电力, 2025, 58(3): 151-161.
[2]	姜文瑾, 刘巧妹, 杨晓东, 阙定飞, 沈豫, 黄夏楠, 赖振华. 计及气固两相储氢特性的海上风电-多元储能系统优化配置[J]. 中国电力, 2024, 57(9): 103-112.
[3]	徐峰亮, 王克谦, 王文豪, 王鹏, 王文烨, 张帅, 赵凤展. 计及激励型需求响应的低压配电网混合储能优化配置[J]. 中国电力, 2024, 57(6): 90-101.
[4]	王扬, 路菲, 李骥, 谈竹奎, 孙宗宇, 徐伟, 宋子宏, 刘振鹏. 基于综合优化目标的低碳园区能源系统规划配置[J]. 中国电力, 2024, 57(4): 14-24.
[5]	李虎军, 张栋, 吕梦璇, 邓方钊, 杨萌, 元博. 考虑碳排放约束的新能源与调峰资源优化配置方法[J]. 中国电力, 2024, 57(4): 42-51.
[6]	袁铁江, 杨洋, 李瑞, 蒋东方. 考虑源荷不确定性的氢能微网容量优化配置[J]. 中国电力, 2023, 56(7): 21-32.
[7]	窦真兰, 张春雁, 赵慧荣, 姚禹琦, 彭道刚. 含氢能汽车负荷的住宅光-氢耦合能源系统容量优化配置[J]. 中国电力, 2023, 56(7): 54-65.
[8]	孙东磊, 王耀, 张惠雯, 刘蕊, 石冰珂. 基于矩差分析的配电网分布式储能优化配置[J]. 中国电力, 2023, 56(12): 31-40.
[9]	王霄鹤, 杨海超, 宋爽, 杨玉新, 殷贵, 王克. 海上风电场经66 kV海缆送出系统过电压机理[J]. 中国电力, 2023, 56(11): 29-37, 48.
[10]	王小飞, 任洪波, 吴琼, 李琦芬. 考虑中长期碳减排约束的区域综合能源系统多阶段动态规划[J]. 中国电力, 2023, 56(11): 185-196.
[11]	胡臻达, 姜文瑾, 张林垚, 杨晓东, 邹艺超, 王凯. 基于改进猫群算法的氢储能容量优化配置[J]. 中国电力, 2023, 56(10): 33-42.
[12]	陈子含, 滕伟, 胥学峰, 丁显, 柳亦兵. 基于图卷积网络和风速差分拟合的中长期风功率预测[J]. 中国电力, 2023, 56(10): 96-105.
[13]	赵安军, 王鹏柱, 荆竞, 高之坤, 李旺. 考虑电动汽车影响的农村家庭新能源容量优化配置方法[J]. 中国电力, 2022, 55(8): 31-39,50.
[14]	周诗嘉, 杨光源, 彭光强, 武霁阳, 辛清明. 基于多相风力发电系统的容错控制策略研究[J]. 中国电力, 2022, 55(7): 134-141.
[15]	刘櫂芮, 贾祺, 严干贵, 翟文超, 孙勇, 李宝聚. 基于惯量响应的双馈风电机组动态协调机理研究[J]. 中国电力, 2022, 55(7): 142-151.

模态框（Modal）标题

基于猫鼠种群算法的分散式风力发电优化配置

Optimal Configuration for Distributed Wind Generation Based on Cat and Mouse Swarm Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics