基于智能水滴算法的火电机组发电调度问题研究
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摘要
电力系统机组组合是编制短期发电计划所需解决的核心问题,其所具有的高维数、非凸、离散、非线性等特征导致迄今尚未找到确定该问题最优解的解析算法。采用新型群智能算法——智能水滴算法,动态地生成每一时段的机组可行组合方案集合,基于此构建水滴遍历的空间,并将机组组合中各类技术经济约束条件融入智能水滴的评价与更新策略机制,提出了机组组合问题的智能水滴算法建模机制及求解流程。通过大量智能水滴并行运行、共同作用,得到了机组组合优化方案。将提出的模型应用于典型算例,计算结果证明,智能水滴算法能够有效解决机组组合问题,并且具有更强的全局搜索能力,收敛速度快,寻优迭代次数少,值得进一步研究探讨。
The unit combination of power system is the core problem to be solved in the preparation of short-term power generation plan. Due to its characteristics of high dimension, non-convex, discrete and non-linear, an analytical algorithm to determine the optimal solution has not been found so far. This research adopted the new swarm intelligence algorithm-intelligent water droplets algorithm, which dynamically generated feasible unit set combination for each time interval, thereupon constructed water droplets traversal space, and integrated various technical and economic constraints of unit combination into the intelligent water droplet evaluation and update strategy mechanism. The modeling mechanism and solution flow of intelligent water drop algorithm for unit combination problem were proposed. Through the parallel operation and joint action of a large number of intelligent water droplets, the unit combination optimization scheme is obtained. The proposed model is applied to a typical calculation example, and the results show that the intelligent droplet algorithm can effectively solve the unit combination problem, and has stronger global search ability, fast convergence speed, and fewer optimization iterations, which is worthy of further study and discussion.
The unit combination of power system is the core problem to be solved in the preparation of short-term power generation plan. Due to its characteristics of high dimension, non-convex, discrete and non-linear, an analytical algorithm to determine the optimal solution has not been found so far. This research adopted the new swarm intelligence algorithm-intelligent water droplets algorithm, which dynamically generated feasible unit set combination for each time interval, thereupon constructed water droplets traversal space, and integrated various technical and economic constraints of unit combination into the intelligent water droplet evaluation and update strategy mechanism. The modeling mechanism and solution flow of intelligent water drop algorithm for unit combination problem were proposed. Through the parallel operation and joint action of a large number of intelligent water droplets, the unit combination optimization scheme is obtained. The proposed model is applied to a typical calculation example, and the results show that the intelligent droplet algorithm can effectively solve the unit combination problem, and has stronger global search ability, fast convergence speed, and fewer optimization iterations, which is worthy of further study and discussion.
引文
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[13] SHAH-HOSSEINI H.Optimization with the Nature-Inspired Intelligent Water Drop Algorithm[J].Journal of Intelligent Computing and Cybernetics.2008,1(2):193-212.
[2] TONG S K,SHAHIDEPOUR S M.Hydrothermal Unit Commitment with Probabilistic Constrants Unising Segmentation Method[J].IEEE Trans on PWRS,1990,5(1):276-282.
[3] 范宏,韦化.基于改进遗传算法及其在机组优化组合中的应用[J].电力系统及其自动化学报,2004,16(4):18-20.FAN Hong,WEI Hua.Improved Genetic Algorithm And Its Application in Unit Commitment Optimization[J].Proceedings of the EPSA,2004,16(4):18-20.
[4] MAIFELD T T,SHEBLE G B.Genetic-Based Unit Commitment[J].IEEE Trans on Power System,1996,11(3):1359-1370.
[5] 黄平,张尧,曾红.改进的粒子群优化算法求解电力经济调度问题[J].华中科技大学学报,2010,38(1):121-124.HUANG Ping,ZHANG Yao,ZENG Hong.Improved Particle Swarm Optimization Sloving Economic Dicpatch of Electric Power[J].Journal of Huazhong University of Science and Technology,2010,38(1):121-124.
[6] 王楠,张粒子,舒隽,等.基于粒子群修正策略的机组组合解耦算法[J].电网技术,2010,34(1):79-83.WANG Nan,ZHANG Lizi,SHU Jun,et al.Decoupling Algorithms for Unit Commitment Based on Modification of Particle Swarm Optimization[J].Power System Technology,2010,34(1):79-83.
[7] 郝晋.蚁群优化算法及其在电力系统短期发电计划中的应用研究[D].重庆:重庆大学电气工程学院,2002.
[8] SHAH-HOSSEINI H.Problem Solving by Intelligent Water Drop[C]// IEEE Congress on Evolutionary Computation,Singapore:2007.
[9] 孙惠娟,彭春华,易洪京.大规模风电接入电网多目标随机优化调度[J] .电力自动化设备,2012,32(5):123-128.SUN Huijuan,PENG Chunhua,YI Hongjing.Multi-objective Stochastic Optimal Dispatch of Power System with Wind Farms[J].Electric Power Automation Equipment,2012,32(5):123-128.
[10] NAGALAKSHMI P,HARISH Y,KUMAR R K ,et al.Combined Economic and Emission Dispatch Using Intelligent Water Drops-continuous Optimization Algorithm[C]//International Conference on Recent Advancements in Electrical,Electronics and Control Engineering:2011.
[11] DUAN H,LIU S,WU J.Novel Intelligent Water Drops Optimization Approach to Single UCAV Smooth Trajectory Planning[J].Aerospace Science and Technology,2009,(13):442-449.
[12] RAO S.An Intelligent Water Drop Algorithm for Solving Economic Load Dispatch Problem[J].World Academy of Science,Engineering and Technology,2011,58:923-929.
[13] SHAH-HOSSEINI H.Optimization with the Nature-Inspired Intelligent Water Drop Algorithm[J].Journal of Intelligent Computing and Cybernetics.2008,1(2):193-212.