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Volume 6 Issue 5
Sep.  2019

IEEE/CAA Journal of Automatica Sinica

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Junqing Li, Quanke Pan, Peiyong Duan, Hongyan Sang and Kaizhou Gao, "Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1240-1250, Sept. 2019. doi: 10.1109/JAS.2017.7510454
Citation: Junqing Li, Quanke Pan, Peiyong Duan, Hongyan Sang and Kaizhou Gao, "Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1240-1250, Sept. 2019. doi: 10.1109/JAS.2017.7510454

Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

doi: 10.1109/JAS.2017.7510454
Funds:

the National Natural Science Foundation of China 61773192

the National Natural Science Foundation of China 61773246

the National Natural Science Foundation of China 61603169

the National Natural Science Foundation of China 61803192

Shandong Province Higher Educational Science and Technology Program J17KZ005

Special Fund Plan for Local Science and Technology Development Lead by Central Authority, and Major Basic Research Projects in Shandong ZR2018ZB0419

More Information
  • In this study, we present a Pareto-based chemical-reaction optimization (PCRO) algorithm for solving the multi-area environmental/economic dispatch optimization problems. Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a self-adaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore, a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

     

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  • [1]
    J. H. Talaq, F. El-Hawary, and M. E. El-Hawary, "A summary of environmental/economic dispatch algorithms, " IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1508-1516, Aug. 1994. http://openurl.ebscohost.com/linksvc/linking.aspx?stitle=IEEE%20Transactions%20on%20Power%20Systems&volume=9&issue=3&spage=1508
    [2]
    A. Farag, S. Al-Baiyat, and T. C. Cheng, "Economic load dispatch multiobjective optimization procedures using linear programming techniques, " IEEE Trans. Power Syst., vol. 10, no. 2, pp. 731-738, May 1995. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=387910
    [3]
    M. A. Abido, "Multiobjective evolutionary algorithms for electric power dispatch problem, " IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 315- 329, Jun. 2006. http://www.springerlink.com/content/J26286562258752X
    [4]
    M. A. Abido, "A novel multiobjective evolutionary algorithm for environmental/economic power dispatch, " Electr. Power Syst. Res., vol. 65, no. 1, pp. 71-81, Apr. 2003. http://www.sciencedirect.com/science/article/pii/S0378779602002213
    [5]
    M. A. Abido, "A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch, " Int. J. Electr. Power Energy Syst., vol. 25, no. 2, pp. 97-105, Feb. 2003. http://www.sciencedirect.com/science/article/pii/S0142061502000273
    [6]
    R. T. F. A. King, H. C. S. Rughooputh, and K. Deb, "Evolutionary multi-objective environmental/economic dispatch: Stochastic versus deterministic approaches, " in Proc. 3rd Int. Conf. Evolutionary Multi-Criterion Optimization, Berlin Heidelberg, Germany, 2005, pp. 677-691.
    [7]
    S. Agrawal, B. K. Panigrahi, and M. K. Tiwari, "Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch, " IEEE Trans. Evol. Comput., vol. 12, no. 5, pp. 529-41, Oct. 2008. http://ieeexplore.ieee.org/document/4454712/
    [8]
    Y. Zhang, D. W. Gong, and Z. H. Ding, "A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch, " Inf. Sci., vol. 192, pp. 213-227, Jun. 2012. http://www.sciencedirect.com/science/article/pii/S0020025511002787
    [9]
    Z. H. Xia, X. H. Wang, X. M. Sun, and Q. Wang, "A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data, " IEEE Trans. Parall. Distrib. Syst., vol. 27, no. 2, pp. 340-352, Feb. 2015. http://openurl.ebscohost.com/linksvc/linking.aspx?stitle=IEEE%20Transactions%20on%20Parallel%20and%20Distributed%20Systems&volume=27&issue=2&spage=340
    [10]
    Z. J. Fu, K. Ren, J. G. Shu, X. M. Sun, and F. X. Huang, "Enabling personalized search over encrypted outsourced data with efficiency improvement, " IEEE Trans. Parall. Distrib. Syst., vol. 27, no. 9, pp. 2546- 2559, Sep. 2016. http://ieeexplore.ieee.org/document/7349214
    [11]
    P. Guo, J. Wang, B. Li, and S. Lee, "A variable thresholdvalue authentication architecture for wireless mesh networks, " J. Internet Technol., vol. 15, no. 6, pp. 929-936, 2014.
    [12]
    Z. J. Fu, X. M. Sun, Q. Liu, L. Zhou, and J. G. Shu, "Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing, " IEICE Trans. Commun., vol. E98.B, no. 1, pp. 190-200, Jan. 2015.
    [13]
    Y. J. Ren, J. Shen, J. Wang, J. Han, and S. Y. Lee, "Mutual verifiable provable data auditing in public cloud storage, " J. Internet Technol., vol. 16, no. 2, pp. 317-323, Mar. 2015.
    [14]
    T. H. Ma, J. J. Zhou, M. L. Tang, Y. Tian, A. Al-Dhelaan, M. Al-Rodhaan, and S. Lee, "Social network and tag sources based augmenting collaborative recommender system, " IEICE Trans. Inf. Syst., vol. E98.D, no. 4, pp. 902-910, Apr. 2015.
    [15]
    J. Y. Li, M. K. Qiu, Z. Ming, G. Quan, X. Qin, and Z. H. Gu, "Online optimization for scheduling preemptable tasks on IaaS cloud systems, " J. Parall. Distrib. Comp., vol. 72, no. 5, pp. 666-677, May 2012. http://www.sciencedirect.com/science/article/pii/S0743731512000366
    [16]
    V. H. Quintana, R. Lopez, R. Romano, and V. Valadez, "Constrained economic dispatch of multi-area systems using the Dantzig-Wolfe decomposition principle, " IEEE Trans. Power Appar. Syst., vol. PAS-100, no. 4, pp. 2127-37, Apr. 1981.
    [17]
    D. Streiffert, "Multi-area economic dispatch with tie line constraints, " IEEE Trans. Power Syst., vol. 10, no. 4, pp. 1946-51, Nov. 1995.
    [18]
    C. Wang and S. M. Shahidehpour, "A decomposition approach to nonlinear multi-area generation scheduling with tie-line constraints using expert systems, " IEEE Trans. Power Syst., vol. 7, no. 4, pp. 1409-1418, Nov. 1992. http://openurl.ebscohost.com/linksvc/linking.aspx?stitle=IEEE%20Transactions%20on%20Power%20Systems&volume=7&issue=4&spage=1409
    [19]
    L. F. Wang and C. Singh, "Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search, " Eng. Appl. Artif. Intell., vol. 22, no. 2, pp. 298-307, Mar. 2009. http://www.sciencedirect.com/science/article/pii/S0952197608001322
    [20]
    M. Basu, "Teaching-learning-based optimization algorithm for multi-area economic dispatch, " Energy, vol. 68, pp. 21-28, Apr. 2014. http://www.sciencedirect.com/science/article/pii/S0360544214001984
    [21]
    A. Y. S. Lam, J. L. Xu, and V. O. K. Li, "Chemical reaction optimization for population transition in peer-to-peer live streaming, " in Proc. IEEE Congr. Evolutionary Computation (CEC), Barcelona, Spain, 2010, pp. 1 -8.
    [22]
    A. Y. S. Lam and V. O. K. Li, "Chemical-reaction-inspired metaheuristic for optimization, " IEEE Trans. Evol. Comput., vol. 14, no. 3, pp. 381- 399, Jun. 2010. http://ieeexplore.ieee.org/document/5353674
    [23]
    A. Lam, V. O. K. Li, and J. J. Q. Yu, "Real-coded chemical reaction optimization, " IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 339-353, Jun. 2012.
    [24]
    A. Y. S. Lam and V. O. K. Li, "Chemical reaction optimization: a tutorial, " Memetic Comp., vol. 4, no. 1, pp. 3-17, Mar. 2012.
    [25]
    M. A. Abido, "Multiobjective particle swarm optimization for environmental/economic dispatch problem, " Electr. Power Syst. Res., vol. 79, no. 7, pp. 1105-1113, Jul. 2009. http://www.sciencedirect.com/science/article/pii/S0378779609000388
    [26]
    J. Li, Q. Pan, and S. Xie, "An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems, " Appl. Math. Comput., vol. 218, no. 18, pp. 9353-9371, 2012. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7d03e6fe3ecb3cddabb02a8b8384ba59
    [27]
    K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ, " IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, Apr. 2002.
    [28]
    S. X. Yang, M. Q. Li, X. H. Liu, and J. H. Zheng, "A grid-based evolutionary algorithm for many-objective optimization, " IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 721-736, Oct. 2013.
    [29]
    Q. K. Pan, M. F. Tasgetiren, P. N. Suganthan, and T. J. Chua, "A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem, " Inf. Sci., vol. 181, no. 12, pp. 2455-2468, Jun. 2011. http://en.cnki.com.cn/Article_en/CJFDTotal-ZGJX201118012.htm
    [30]
    J. Q. Li, S. C. Bai, P. Y. Duan, H. Y. Sang, Y. Y. Han, and Z. X. Zheng, "An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system, " Int. J. Prod. Res., 2018, doi: 10.1080/00207543.2019.1571687.
    [31]
    P. K. Tripathi, S. Bandyopadhyay, and S. K. Pal, "Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients, " Inf. Sci., vol. 177, no. 22, pp. 5033-5049, Nov. 2007. http://www.sciencedirect.com/science/article/pii/S0020025507003155
    [32]
    S. Mostaghim and J. Teich, "Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), " in Proc. IEEE Swarm Intelligence Symp., Indianapolis, IN, USA, 2003, pp. 26-33.
    [33]
    C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization, " IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 256-279, Jun. 2004. http://ieeexplore.ieee.org/iel5/4235/28981/01304847.pdf
    [34]
    J. Zhao, S. X. Liu, M. C. Zhou, X. W. Guo, and Q. Liang, "Modified cuckoo search algorithm to solve economic power dispatch optimization problems, " IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp.794-806, Mar. 2018. http://www.cnki.com.cn/Article/CJFDTotal-ZDHB201804005.htm

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    Highlights

    • we present a Pareto-based chemical-reaction optimization (PCRO) algorithm for solving the multi-area environmental/economic dispatch optimization problems.
    • a novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is developed.
    • an ensemble of effective neighborhood approaches is developed, and a self-adaptive neighborhood structure selection mechanism is also embedded.
    • a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front.
    • a kinetic-energy-based search procedure is developed to enhance the global search ability.

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