您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (4): 1-9.doi: 10.6040/j.issn.1672-3961.0.2017.369

• 机器学习与数据挖掘 •    下一篇

计及排放的动态经济调度免疫克隆演化算法

钱淑渠1,武慧虹1,徐国峰2,金晶亮3   

  1. 1. 安顺学院数理学院, 贵州 安顺 561000;2. 南京工程学院计算中心, 江苏 南京 210016;3. 南通大学理学院, 江苏 南通 226000
  • 收稿日期:2017-05-20 出版日期:2018-08-20 发布日期:2017-05-20
  • 作者简介:钱淑渠(1978— ), 男, 安徽枞阳人, 副教授, 工学博士, 主要研究方向为计算智能,系统建模与控制. E-mail:shuquqian@163.com
  • 基金资助:
    国家自然科学基金资助项目(61762001,71603135);贵州省科学技术基金资助项目(黔科合J字［2015］2002,黔科合LH字［2017］7047);贵州省教育厅优秀科技创新人才奖励计划资助项目(黔教合KY字［2014］255);南京工程学院创新基金面上资助项目II(CKJC201603);江苏省高校创新基金资助项目(KYLX15-0274)

Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission

QIAN Shuqu1, WU Huihong1, XU Guofeng2, JIN Jingliang3   

  1. 1. School of Sciences, Anshun University, Anshun 561000, Guizhou, China;
    2. Computing Center, University of Engineering of Nanjing, Nanjing 210016, Jiangsu, China;
    3. School of Sciences, Nantong University, Nantong 226000, Jiangsu, China
  • Received:2017-05-20 Online:2018-08-20 Published:2017-05-20

PDF (PC)

614

摘要： 结合免疫系统的克隆选择原理和遗传进化机制,提出一种免疫克隆演化算法(Immune clonal evolutionary algorithm, ICEA)。ICEA建立克隆选择机制与演化机制的动态结合,提出动态免疫选择和自适应非均匀突变算子,针对动态经济调度(dynamic emission economic dispatch, DEED)问题特性引入不同的等式和不等式的约束修补策略,使其适合大规模约束的DEED问题求解。数值试验将ICEA应用于10机系统进行测试,并与同类算法展开比较。仿真结果表明,ICEA具有较好的收敛性和全局优化效果,获得的Pareto前沿具有较好的均匀性和延展性,该结果能为电力系统调度人员提供较为有效的调度决策方案。

关键词: 动态免疫选择, 进化优化, 动态经济调度, 约束多目标优化, 自适应

Abstract: An immune clonal evolutionary algorithm(ICEA)was proposed by combining the clone selection principle of immune system and the evolution mechanism of genetic algorithm. A kind of dynamic immune selection strategy was introduced and a self-adaption non-uniform mutation operator was proposed. In order to make it suitable for solving dynamic emission economic dispatch(DEED)problem with many constrains, different repair strategies were introduced for the equality and inequality constrains of DEED model. In numerical experiments, ICEAs performance on 10-units system was tested, and several peer algorithms were compared. The simulation results indicated that ICEA had good convergence and global optimization efficiency. The uniformity and ductility of the Pareto optimal frontier obtained by ICEA was better than that of comparison algorithms. The Pareto optimal frontier could provide a more efficient scheduling decision-making approach for power system dispatcher.

Key words: evolution optimization, constrained multiobjective optimization, dynamic immune selection, self-adaption, dynamic economic dispatch

中图分类号: 

  • TP306.1
[1] ZHONG H, XIA Q, WANG Y, et al. Dynamic economic dispatch considering transmission losses using quadratically constrained quadratic program method[J]. IEEE Transactions on Power Systems, 2013, 28(3):2232-2241.
[2] HOSSEINNEZHAD V, BABAEI E. Economic load dispatch using θ-PSO[J]. International Journal of Electrical Power & Energy Systems, 2013, 49(7):160-169.
[3] 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. JIANG Xingwen, ZHOU Jianzhong, WANG Hao, et al. Modeling and solving for dynamic economic emission dispatch of power system[J]. Power System Technology, 2013, 37(2):385-391.
[4] NWULU N I, XIA X. Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs[J]. Energy Conversion & Management, 2015, 89:963-974.
[5] 朱永胜, 王杰, 瞿博阳,等. 含风电场的多目标动态环境经济调度[J]. 电网技术, 2015, 39(5):1315-1322. ZHU Yongsheng, WANG Jie, QU Boyang, et al. Multi-objective dynamic economic emission dispatching of power grid containing wind farms[J]. Power System Technology, 2015, 39(5):1315-1322.
[6] YANG L, FRAGA E S, PAPAGEORGIOU L G. Mathematical programming formulations for non-smooth and non-convex electricity dispatch problems[J]. Electric Power Systems Research, 2013, 95(1):302-308.
[7] IRINA Ciornei, KYRIAKIDES Elias. Recent methodologies and approaches for the economic dispatch of; generation in power systems[J]. International Transactions on Electrical Energy Systems, 2013, 23(7):1002-1027.
[8] ZHU T, LUO W, BU C, et al. Accelerate population-based stochastic search algorithms with memory for optima tracking on dynamic power systems[J]. IEEE Transactions on Power Systems, 2015, 31(1):268-277.
[9] 罗中良. 经济调度问题的混合蚁群算法及序列二次规划法解[J]. 计算机应用研究, 2007, 24(6):112-114. LUO Zhongliang. Ant colony algorithm and its convergence for economic dispatch problem with valve-point effect[J]. Journal of Computer Applications, 2007, 24(6):112-114.
[10] CIORNEI I, KYRIAKIDES E. Recent methodologies and approaches for the economic dispatch of generation in power systems[J]. International Transactions on Electrical Energy Systems, 2013, 23(7):1002-1027.
[11] 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.
[12] BASU M. Multi-objective differential evolution for dynamic economic emission dispatch[J]. International Journal of Emerging Electric Power Systems, 2014, 15(2):141-150.
[13] 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.
[14] QU B Y, LIANG J J, ZHU Y S, et al. Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method[J]. Natural Computing, 2017:1-9.
[15] CASTRO L N D, ZUBEN F J V. Learning and optimization using the clonal selection principle [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(3):239-251.
[16] 左万利, 韩佳育, 刘露,等. 基于人工免疫算法的增量式用户兴趣挖掘[J]. 计算机科学, 2015, 42(5):34-41. ZUO Wanli, HAN Jiayu, LIU Lu, et al. Incremental user interest mining based on artificial immune algorithm[J]. Computer Science, 2015, 42(5):34-41.
[17] 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. WENG Zhenxing, SHI Libao, XU Zheng, et al. Power system dynamic economic dispatch incorporating wind power Cos[J]. Proceedings of the CSEE, 2014, 34(4):514-523.
[18] 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. LI Dan, GAO Liqun, WANG Ke, et al. Dynamic double-population particle swarm optimization algorithm for power system unit commitment[J]. Journal of Computer Applications, 2008, 28(1): 104-107.
[19] 覃晖, 周建中. 基于多目标文化差分进化算法的水火电力系统优化调度[J]. 电力系统保护与控制, 2011, 39(22):90-97. QIN Hui, ZHOU Jianzhong. Optimal hydrothermal scheduling based on multi-objective cultured differential evolution[J]. Power System Protection and Control, 2011, 39(22):90-97.
[20] DEB K, AGRAWAl R B. Simulated binary crossover for continuous search space[J]. Complex Systems, 1994, 9(3):115-148.
[21] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[1] 程春蕊,毛北行. 一类非线性混沌系统的自适应滑模同步[J]. 山东大学学报 (工学版), 2020, 50(5): 1-6.
[2] 王春彦,邸金红,毛北行. 基于新型趋近律的参数未知分数阶Rucklidge系统的滑模同步[J]. 山东大学学报 (工学版), 2020, 50(4): 40-45.
[3] 刘保成,朴燕,宋雪梅. 联合检测的自适应融合目标跟踪[J]. 山东大学学报 (工学版), 2020, 50(3): 51-57.
[4] 闫威,张达敏,张绘娟,辛梓芸,陈忠云. 基于混合决策的改进鸟群算法[J]. 山东大学学报 (工学版), 2020, 50(2): 34-43.
[5] 张胜男,王雷,常春红,郝本利. 基于三维剪切波变换和BM4D的图像去噪方法[J]. 山东大学学报 (工学版), 2020, 50(2): 83-90.
[6] 曹小洁,李小华,刘辉. 一类非仿射非线性大系统的结构在线扩展[J]. 山东大学学报 (工学版), 2020, 50(1): 35-48.
[7] 苏佳林,王元卓,靳小龙,程学旗. 自适应属性选择的实体对齐方法[J]. 山东大学学报 (工学版), 2020, 50(1): 14-20.
[8] 刘美珍,周风余,李铭,王玉刚,陈科. 基于模型不确定补偿的轮式移动机器人反演复合控制[J]. 山东大学学报 (工学版), 2019, 49(6): 36-44.
[9] 马川,刘彦呈,刘厶源,张勤进. 考虑未知死区非线性的自适应模糊神经UUV航迹跟踪控制[J]. 山东大学学报 (工学版), 2019, 49(3): 47-56.
[10] 李进,李二超. 基于正态分布和自适应变异算子的ε截断算法[J]. 山东大学学报 (工学版), 2019, 49(2): 47-53.
[11] 刘洪铭,曾鸿雁,周伟,王涛. 基于改进粒子群算法作业车间调度问题的优化[J]. 山东大学学报 (工学版), 2019, 49(1): 75-82.
[12] 李尧,王志海,孙艳歌,张伟. 一种基于深度属性加权的数据流自适应集成分类算法[J]. 山东大学学报 (工学版), 2018, 48(6): 44-55, 66.
[13] 牟廉明. 自适应特征选择加权k子凸包分类[J]. 山东大学学报 (工学版), 2018, 48(5): 32-37.
[14] 马驰骋, 郭宗和, 刘灿昌, 代祥俊, 张希农, 毛伯永. 变质量弹性梁结构动力学特性[J]. 山东大学学报 (工学版), 2018, 48(4): 78-87.
[15] 张博涵,陈哲明,付江华,陈宝. 四轮独立驱动电动汽车自适应驱动防滑控制[J]. 山东大学学报(工学版), 2018, 48(1): 96-103.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 王汝贵,蔡敢为 . 两自由度可控平面连杆机构机电耦合系统的超谐波共振分析[J]. 山东大学学报(工学版), 2008, 38(3): 58 -63 .
[2] 贾超,赵建宇,徐帮树,岳长城,李树忱 . 清水隧道围岩软土振动液化研究[J]. 山东大学学报(工学版), 2008, 38(1): 83 -87 .
[3] 余嘉元1 , 田金亭1 , 朱强忠2 . 计算智能在心理学中的应用[J]. 山东大学学报(工学版), 2009, 39(1): 1 -5 .
[4] 邓斌,王江 . 基于混沌同步与自适应控制的神经元模型参数估计[J]. 山东大学学报(工学版), 2007, 37(5): 19 -23 .
[5] 高厚磊 田佳 杜强 武志刚 刘淑敏. 能源开发新技术——分布式发电[J]. 山东大学学报(工学版), 2009, 39(5): 106 -110 .
[6] 季涛,高旭,孙同景,薛永端,徐丙垠 . 铁路10 kV自闭/贯通线路故障行波特征分析[J]. 山东大学学报(工学版), 2006, 36(2): 111 -116 .
[7] 丑武胜 王朔. 大刚度环境下力反馈主手自适应算法研究[J]. 山东大学学报(工学版), 2010, 40(1): 1 -5 .
[8] 李国正1,史淼晶1,李福凤2,王忆勤2. 舌体图像分割技术的实验分析与改进[J]. 山东大学学报(工学版), 2010, 40(5): 87 -95 .
[9] 张波,李术才,杨学英,王锡平,张敦福 . 含两个圆形孔洞岩盐路基稳定性的数值分析[J]. 山东大学学报(工学版), 2008, 38(1): 66 -69 .
[10] 胡天亮1 ，张承瑞1 ，杨林1 ，武洪恩2 . 一种高速可配置实时总线的开发及其应用[J]. 山东大学学报(工学版), 2009, 39(1): 96 -101 .
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发