基于修补策略的约束多目标动态环境经济调度优化算法

doi:10.11772/j.issn.1001-9081.2015.08.2249

计算机应用 ›› 2015, Vol. 35 ›› Issue (8): 2249-2255.DOI: 10.11772/j.issn.1001-9081.2015.08.2249

基于修补策略的约束多目标动态环境经济调度优化算法

钱淑渠¹, 武慧虹¹, 徐国峰²

1. 安顺学院数理学院, 贵州安顺 561000;
2. 南京工程学院工业中心, 南京 210016

收稿日期:2015-02-12 修回日期:2015-03-25 出版日期:2015-08-10 发布日期:2015-08-14
通讯作者: 钱淑渠(1978-),男,安徽枞阳人,副教授,博士研究生,主要研究方向:智能计算、系统建模及控制,shuquqian@163.com
作者简介:武慧虹(1980-),女,山西太原人,副教授,硕士,主要研究方向:优化算法、群与图; 徐国峰(1976-),男,湖北武穴人,讲师,主要研究方向:重复控制、电力电子。
基金资助:
国家自然科学基金资助项目(61304146);贵州省教育厅优秀科技创新人才奖励计划项目(黔教合KY字[2014]255);贵州省科学技术基金资助项目(20152002)。

Constrained multiobjective optimization algorithm based on repairing strategy for solving dynamic environment/economic dispatch

QIAN Shuqu¹, WU Huihong¹, XU Guofeng²

1. School of Sciences, Anshun University, Anshun Guizhou 561000, China;
2. Industrial Center, Nanjing Institute of Technology, Nanjing Jiangsu 210016, China

Received:2015-02-12 Revised:2015-03-25 Online:2015-08-10 Published:2015-08-14

摘要/Abstract

摘要：

针对传统的优化算法求解多目标动态环境经济调度(MODEED)模型时极难获得高质量的可行解,且收敛速度慢等问题,根据MODEED模型约束特征,设计了一种约束修补策略;然后将该策略嵌入非支配排序算法(NSGAⅡ),进而提出一种修补策略的约束多目标优化算法(CMEA/R);接着借助模糊决策理论给出了多目标问题的最优决策向量;最后,以经典的10机系统为例,验证了CMEA/R的求解能力,并比较了不同群体规模下CMEA/R与NSGAⅡ的性能。仿真结果表明,在不同群体规模下,与NSGAⅡ相比,CMEA/R的污染排放平均减少了480 lb(217.7 kg),燃料成本平均减少了7 800美元,执行时间平均减少了0.021 s;覆盖率(HR)性能优于NSGAⅡ,且收敛速度较NSGAⅡ快。

关键词: 电力系统, 动态环境经济调度, 多目标优化, 修补策略, 收敛性

Abstract:

The classical multiobjectve optimization algorithm is difficult to achieve high quality feasible solutions on Multiobjectve Dynamic Environment/Economic Dispatch (MODEED) model, and shows a slower convergence speed. Firstly, a new constraint repairing strategy based on the constraint characteristic of MODEED was developed. Secondly, the proposed repairing approach was inserted into the Nondominated Sorting Genetic Algorithm-Ⅱ (NSGAⅡ), and a Constrained Multiobjective Evolutionary Algorithm based on repairing Strategy (CMEA/R) was proposed. Thirdly, fuzzy decision theory was applied to determine the best compromise solution of the MODEED. Finally, to validate the optimization ability of the CMEA/R, it was applied to solve the MODEED problem of standard IEEE 30-bus 10-generator system, and a comparative analysis with NSGAⅡ was presented under various population size. The simulation results revealed that the pollutant emission and fuel cost obtained by CMEA/R were reduced by 480 lb (217.7 kg) and 7 800 dollar, respectively, the average implication time was reduced by 0.021 second. Furthermore, CMEA/R shows a superior performance in terms of Hypervolume Rate (HR) indicator and convergence ability.

Key words: power system, dynamic environment/economic dispatch, multiobjective optimization, repairing strategy, convergence

中图分类号:

TP18
TP301.6

钱淑渠, 武慧虹, 徐国峰. 基于修补策略的约束多目标动态环境经济调度优化算法[J]. 计算机应用, 2015, 35(8): 2249-2255.

QIAN Shuqu, WU Huihong, XU Guofeng. Constrained multiobjective optimization algorithm based on repairing strategy for solving dynamic environment/economic dispatch[J]. Journal of Computer Applications, 2015, 35(8): 2249-2255.

参考文献

[1] CAO Y, WANG G. Research on power system complexity and related topics [J]. Electric Power Automation Equipment, 2010, 30(2): 5-10. (曹一家,王光增.电力系统复杂性及其相关问题研究[J].电力自动化设备,2010,30(2):5-10.)
[2] ZHANG L, YE T, XIN Y, et al. Problems and measures of power grid accommodating large scale wind power [J]. Proceedings of the Chinese Society for Electrical Engineering, 2010, 30(25): 1-9. (张丽英,叶廷路,辛耀中,等.大规模风电接入电网的相关问题及措施[J].中国电机工程学报,2010,30(25):1-9.)
[3] CIORNEI I, KYRIAKIDES E. Recent methodologies and approa-ches for the economic dispatch of generation in power systems [J]. International Transactions on Electrical Energy Systems, 2013, 23(7): 1002-1027.
[4] LI D, GAO L, WANG K, et al. Dynamic double-population particle swarm optimization algorithm for power system unit commitment [J]. Journal of Computer Applications, 2008, 28(1): 104-107. (李丹,高立群,王珂,等.电力系统机组组合问题的动态双种群粒子群算法[J].计算机应用,2008,28(1):104-107.)
[5] PLATBROOD L, CAPITANESCU F, MERCKX C, et al. A generic approach for solving nonlinear-discrete security-constrained optimal power flow problems in large-scale systems [J]. IEEE Transactions on Power Systems, 2014, 29(3): 1194-1203.
[6] WANG L, HUANG F, LI L. Economic distribution of power sys-tems based on hybrid differential evolution with double populations [J]. Control and Decision, 2009, 24(8): 1156-1160. (王凌,黄付卓,李灵坡.基于混合双种群差分进化的电力系统经济负荷分配[J].控制与决策,2009,24(8):1156-1160.)
[7] ZHANG Y, GONG D, GENG N, et al. Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects [J]. Applied Soft Computing, 2014, 18: 248-260.
[8] SRINIVASA RAO B, VAISAKH K. Multi-objective adaptive clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty [J]. International Journal of Electrical Power & Energy Systems, 2013, 53: 390-408.
[9] GUO C, ZHU C, ZHAO B, et al. Power system reactive optimization based on an improved immune algorithm [J]. Automation of Electric Power Systems, 2005, 29(15): 23-29. (郭创新,朱承治,赵波,等.基于改进免疫算法的电力系统无功优化[J].电力系统自动化,2005,29(15):23-29.)
[10] BASU M. Dynamic economic emission dispatch using nondominated sorting genetic algorithm-Ⅱ [J]. International Journal of Electrical Power & Energy Systems, 2008, 30(2): 140-149.
[11] BASU M. Multi-objective Differential evolution for dynamic eco-nomic emission dispatch [J]. International Journal of Emerging Electric Power Systems, 2014, 15(2): 141-150.
[12] QIU W, ZHANG J, WU X, et al. Environmental and economic dispatch based on chaotic multiobjective differential evolution algorithm considering coordinative operation [J]. Electric Power Automation Equipment, 2013, 33(11): 26-31. (邱威,张建华,吴旭,等.采用混沌多目标差分进化算法并考虑协调运行的环境经济调度[J].电力自动化设备,2013,33(11):26-31.)
[13] JIANG X, ZHOU J, WANG H, et al. Modeling and solving for dynamic economic emission dispatch of power system [J]. Power System Technology, 2013, 37(2): 385-391. (江兴稳,周建中,王浩,等.电力系统动态环境经济调度建模与求解[J].电网技术,2013,37(2):385-391.)
[14] CHEN G, CHEN J. Environmental/economic dynamic dispatch modeling and method for power systems integrating wind farms [J]. Proceedings of the Chinese Society for Electrical Engineering, 2013, 33(10): 27-35. (陈功贵,陈金富.含风电场电力系统环境经济动态调度建模与算法[J].中国电机工程学报,2013,33(10):27-35.)
[15] ZHANG X, DONG X. Research on multiobjective scheduling for low-carbon power system with wind farms [J]. Power System Technology, 2013, 37(1): 24-31. (张晓辉,董兴华.含风电场多目标低碳电力系统动态环境经济调度研究[J].电网技术,2013,37(1):24-31.)
[16] WENG Z, SHI L, XU Z, et al. Power system dynamic economic dispatch incorporating wind power cost [J]. Proceedings of the CSEE, 2014, 34(4): 514-523. (翁振星,石立宝,徐政,等.计及风电成本的电力系统动态经济调度[J].中国电机工程学报,2014,34(4):514-523.)
[17] GENT M R, LAMONT J W. Minimum emission dispatch [J]. IEEE Transactions on Power Apparatus and Systems, 1971, 90(6): 2650-2660.
[18] CHEN Y, WEN J, JIANG L, et al. Hybrid algorithm for dynamic economic dispatch with valve-point effects [J]. IET Generation, Transmission & Distribution, 2013, 7(10): 1096-1104.
[19] FARINA M, AMATO P. A fuzzy definition of optimality for many criteria optimization problems [J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2004, 34(3): 315-326.
[20] ATTAVIRIYANUPAP P, KITA H, TANAKA E, et al. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function [J]. IEEE Transactions on Power Systems, 2002, 17(2): 411-416.
[21] ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms: empirical results [J]. Evolutionary Computation, 2000, 8(2): 173-195.

基于修补策略的约束多目标动态环境经济调度优化算法

Constrained multiobjective optimization algorithm based on repairing strategy for solving dynamic environment/economic dispatch

PDF

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]	罗明宇, 骆晓萌, 朱文敏, 张磊, 范秀敏. 船舶狭小空间虚拟人维修姿态建模技术[J]. 计算机应用, 2021, 41(8): 2466-2472.
[2]	张闻强, 邢征, 杨卫东. 基于多区域采样策略的混合粒子群优化求解多目标柔性作业车间调度问题[J]. 计算机应用, 2021, 41(8): 2249-2257.
[3]	张祥飞, 鲁宇明, 张平生. 基于协同进化的约束多目标优化算法[J]. 计算机应用, 2021, 41(7): 2012-2018.
[4]	乔钢柱, 王瑞, 孙超利. 基于分解的高维多目标改进进化算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3097-3103.
[5]	李二超, 李康伟. 基于最小距离和聚合策略的分解多目标进化算法[J]. 计算机应用, 2021, 41(1): 22-28.
[6]	李二超, 杨蓉蓉. 基于自适应反向学习的多目标分布估计算法[J]. 计算机应用, 2021, 41(1): 15-21.
[7]	王博, 刘连生, 韩绍程, 祝世兴. 基于多策略融合的混合多目标蝗虫优化算法[J]. 计算机应用, 2020, 40(9): 2670-2676.
[8]	张凯, 周德云, 杨振, 潘潜. 多约束集群目标下武器-目标分配问题[J]. 计算机应用, 2020, 40(3): 902-911.
[9]	刘静姝, 王莉, 刘惊雷. 无需特征分解的快速谱聚类算法[J]. 计算机应用, 2020, 40(12): 3413-3422.
[10]	陈聿, 田博今, 彭云竹, 廖勇. 联合手肘法和期望最大化的高斯混合聚类电力系统客户分群算法[J]. 计算机应用, 2020, 40(11): 3217-3223.
[11]	郝秦霞. 基于R2指标的高维多目标差分进化推荐式课程系统[J]. 计算机应用, 2020, 40(10): 2951-2959.
[12]	韦姗姗, 胡圣波, 鄢婷婷, 莫金容. 基于分步小波变换的对流层电波传播特性和分析软件[J]. 计算机应用, 2019, 39(6): 1792-1798.
[13]	林立, 孟学雷, 宋仲仲. 考虑区域协调性的城际列车开行方案优化[J]. 计算机应用, 2019, 39(2): 598-603.
[14]	刘明, 董明刚, 敬超. 基于定期竞争学习的多目标粒子群优化算法[J]. 计算机应用, 2019, 39(2): 330-335.
[15]	蔺凯青, 李志华, 郭曙杰, 李双俐. 云环境中多目标优化的虚拟机放置算法[J]. 计算机应用, 2019, 39(12): 3597-3603.