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-  2018 

计及排放的动态经济调度免疫克隆演化算法
Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission

DOI: 10.6040/j.issn.1672-3961.0.2017.369

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

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Abstract:

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。
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

References

[1]  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.
[2]  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.
[3]  BASU M. Multi-objective differential evolution for dynamic economic emission dispatch[J]. International Journal of Emerging Electric Power Systems, 2014, 15(2):141-150.
[4]  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.
[5]  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.
[6]  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.
[7]  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.
[8]  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.
[9]  HOSSEINNEZHAD V, BABAEI E. Economic load dispatch using θ-PSO[J]. International Journal of Electrical Power & Energy Systems, 2013, 49(7):160-169.
[10]  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.
[11]  DEB K, AGRAWAl R B. Simulated binary crossover for continuous search space[J]. Complex Systems, 1994, 9(3):115-148.
[12]  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.
[13]  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.
[14]  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.
[15]  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.
[16]  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.
[17]  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.
[18]  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.
[19]  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.
[20]  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.
[21]  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.

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