Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (1): 11-23.doi: 10.6040/j.issn.1672-3961.0.2020.050

Previous Articles    

Multiobjective dynamic economic emission dispatch differential evolution algorithm based on elites cloning local search

WU Huihong1, QIAN Shuqu1*, LIU Yanmin2, XU Guofeng3, GUO Benhua1   

  1. 1. School of Mathematics and Physics, Anshun University, Anshun 561000, Guizhou, China;
    2. School of Mathematics, Zunyi Normal University, Zunyi 563006, Guizhou, China;
    3. Computing Center, Nanjing Institute of Technology, Nanjing 211167, Jiangsu, China
  • Published:2021-03-01

Abstract: An efficient multiobjective differential evolution algorithm based on elites cloning local search scheme was proposed to solve complex dynamic economic emission dispatch. The conventional differential evolution(DE)algorithm was used as the framework of the proposed algorithm. A cloning operator was developed to enhance the exploration and exploitation ability of elites in the DE algorithm. The elite population to be cloned was established by a dynamic selection mechanism for enhancing the global search ability of the proposed algorithm. To validate the effectiveness of the proposed algorithm, the IEEE 30 bus 10-generator and 15-generator systems were studies as test cases in numerical experiments. The simulation results indicated that the Pareto-optimal front obtained by the proposed algorithm presented a superior performance in convergence and extension over the other reported results recently. As a result, the results were able to provide decision solutions more extensively for decision-makers in power system dispatch.

Key words: dynamic economic emission dispatch, multiobjective optimization, elites cloning, differential evolution, Pareto-optimal front

CLC Number: 

  • TP306.2
[1] ZAMAN M F, ELSAYED S M, RAY T, et al. Evolutionary algorithms for dynamic economic dispatch problems[J]. Power Systems: IEEE Transactions on, 2016, 31(2):1486-1495.
[2] ZHANG Y, GONG D W, GENG N. Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects[J]. Applied Soft Computing, 2014, 18:248-260.
[3] LI X B. Study of multi-objective optimization and multi-attribute decision-making for dynamic economic emission dispatch[J]. Electric Power Components and Systems, 2009, 37(10):1133-1148.
[4] LI Z G, WU W C, ZHANG B M, et al. Dynamic economic dispatch using lagrangian relaxation with multiplier updates based on a Quasi-Newton method[J]. IEEE Transactions on Power Systems, 2013, 28(4):4516-4527.
[5] JEBARAJ L, VENKATESAN C, SOUBACHE I, et al. Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: a review[J]. Renewable and Sustainable Energy Reviews, 2017, 77(9):1206-1220.
[6] LI Z Y, ZOU D X, KONG Z. A harmony search variant and a useful constraint handling method for the dynamic economic emission dispatch problems considering transmission loss[J]. Engineering Applications of Artificial Intelligence, 2019, 84:18-40.
[7] BASU M. Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II[J]. International Journal of Electrical Power and Energy Systems, 2008, 30(2):140-149.
[8] QU B Y, ZHU Y S, JIAO Y C, et al. A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems[J]. Swarm and Evolutionary Computation, 2018, 38:1-11.
[9] ELAIW A M, XIA X, SHEHATA A M. Hybrid DE-SQP and hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve-point effects[J]. Electrical Power System Research, 2013, 103:192-200.
[10] GILL P E, SAUNDERS M. An SQP algorithm for large-scale constrained optimization[J]. Siam Review, 2005, 47(1):99-131.
[11] ZHANG H F, YUE D, XIE X P, et al. Multi-elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch[J]. Applied Soft Computing, 2015, 34:312-323.
[12] ROY P K, BHUI S. A multi-objective hybrid evolutionary algorithm for dynamic economic emission load dispatch[J]. International Transactions on Electrical Energy Systems, 2015, 26(1):49-78.
[13] MASON K, DUGGAN J, HOWLEY E. A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch[J]. International Journal of Electrical Power & Energy Systems, 2018, 100:201-221.
[14] SHEN X, ZOU D X, DUAN N, et al. An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch[J]. Energy, 2019, 186:1-28.
[15] ZHU Y S, QIAO B H,DONG Y, et al. Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2019, 14(4):1-11.
[16] 闫李, 李超, 柴旭朝, 等. 基于多学习多目标鸽群优化的动态环境经济调度[J]. 郑州大学学报(工学版), 2019, 40(4):8-14. YAN Li, LI Chao, CHAI Xuzhao, et al. Dynamic economic emission dispatch based on multiple learning multiobjective pigeon inspired optimization[J]. Journal of Zhengzhou University(Engineering Science), 2019, 40(4):8-14.
[17] 张大, 彭春华, 孙惠娟. 大规模风电机组并网的多目标动态环境经济调度[J]. 华东交通大学学报, 2019, 36(5):129-135. ZHANG Da, PENG Chunhua, SUN Huijuan. Multiobjective dynamic economic emission dispatch of large-scale wind power integration[J]. Journal of East China Jiaotong University, 2019, 36(5):129-135.
[18] STORN R, PRICE K. Differential Evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces[J]. Journal of Global Optimization, 1995, 23(1):119-123.
[19] SARKER R, ABBASS H A. Differential evolution for solving multi-objective optimization problems[J]. ASIA Pacific Journal of Operational Research, 2004, 21(2):225-240.
[20] 钱淑渠,徐国峰,武慧虹, 等. 计及排放的动态经济调度免疫克隆演化算法[J]. 山东大学学报(工学版), 2018, 48(4):1-9. QIAN Shuqu, XU Guofeng, WU Huihong, et al. Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission[J]. Journal of Shandong University(Engineering Science), 2018, 48(4): 1-9.
[1] Runjia SUN,Hainan ZHU,Yutian LIU. Transmission network reconfiguration strategy based on preference multiobjective optimization and genetic algorithm [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 17-23.
[2] QIAN Shuqu, WU Huihong, XU Guofeng, JIN Jingliang. Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(4): 1-9.
[3] ZHANG Shuangsheng, QIANG Jing, LIU Xikun, LIU Hanhu, ZHU Xueqiang. Inverse problems of pollution source identification based on Bayesian-DE [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 131-136.
[4] DENG Guanlong, YANG Hongyong, ZHANG Shuning, GU Xingsheng. Multi-objective scheduling in no-wait flow shop using a hybridized differential evolution algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(5): 21-28.
[5] YANG Longhao, FU Yanggeng, GONG Xiaoting. Parallel differential evolution algorithm for parameter learning of belief rule base [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(1): 30-36.
[6] LIANG Xingjian, ZHAN Zhihui. Improved genetic algorithm based on the dual-mode mutation strategy [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 1-7.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!