基于改进模拟退火遗传算法的梯级水电站长期优化调度

范金骥; FAN Jinji

doi:10.16513/j.cnki.10-1427/tk.2017.04.004

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分布式能源 ›› 2017, Vol. 2 ›› Issue (4) : 20-28. DOI: 10.16513/j.cnki.10-1427/tk.2017.04.004

学术研究

基于改进模拟退火遗传算法的梯级水电站长期优化调度

范金骥 ,
FAN Jinji

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Long-Term Optimal Scheduling of Cascade Hydropower Stations Based on Improved Simulated Annealing Genetic Algorithm

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摘要

Conventional optimization methods have ‘dimension disaster’ problems and poor search efficiencies in solving the long-term scheduling optimization problem of large-scale cascade hydropower stations. In order to achieve better results, the genetic algorithm has been improved in this study. The adaptive control theory is applied to the crossover and mutation operators, such that they can be automatically changed according to the fitness value. Chaos theory is used to generate the initial population, and the simulated annealing method is also introduced. An improved simulated annealing genetic algorithm is proposed by combining the advantages of these two algorithms, which can enhance global optimization capabilities and local search capabilities, and reduce the probability of stuck on local optima. The improved algorithm is then applied to an established model. Through compared with the conventional genetic algorithms, the results show that the improved simulated annealing genetic algorithm has strong global search ability and good solution effect, which can be served as a reference for solving the long-term scheduling optimization problem of large-scale cascade hydropower stations.

关键词

梯级水电站 / 长期优化调度 / 遗传算法 / 混沌算法 / 模拟退火 / cascaded hydropower stations / long-term optimal dispatching / genetic algorithm / chaos algorithm / simulated annealing

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范金骥, FAN

Jinji

. Long-Term Optimal Scheduling of Cascade Hydropower Stations Based on Improved Simulated Annealing Genetic Algorithm[J]. 分布式能源. 2017, 2(4): 20-28 https://doi.org/10.16513/j.cnki.10-1427/tk.2017.04.004

[J]. Distributed Energy Resources. 2017, 2(4): 20-28 https://doi.org/10.16513/j.cnki.10-1427/tk.2017.04.004

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	李安强，张建云，仲志余，等. 长江流域上游控制性水库群联合防洪调度研究[J]. 水利学报，2013, 44(1): 59-66. LI Anqiang, ZHANG Jianyun, ZHONG Zhiyu, et al. Study on joint flood control operation for leading reservoirs in the upper Changjiang River[J]. Journal of Hydraulic Engineering, 2013, 44(1): 59-66. 本文引用 [1]

[2]	王文川，雷冠军，尹航，等. 基于群居蜘蛛优化算法的水库防洪优化调度模型及应用[J]. 水电能源科学，2015, 33(4): 48-51. WANG Wenchuan, LEI Guanjun, YIN Hang, et al. Optimal dispatch model of reservoir flood control based on SSO algorithm[J]. International Journal Hydroelectric Energy, 2015, 33(4): 48-51. 本文引用 [1]

[3]	刘宇，钟平安，张梦然，等. 水库优化调度ANN模型隐层节点数经验公式比较[J]. 水力发电，2013, 39(5): 65-68. LIU Yu, ZHONG Ping'an, ZHANG Mengran, et al. Empirical formulae comparison for number selection of hidden layer's nodes in ANN model applied for reservoir optimal operation[J]. Water Power, 2013, 39(5): 65-68. 本文引用 [1]

[4]	周章贵，董国锋. 合同能源管理——小水电增容改造项目融资新模式[J]. 小水电，2009(4): 32-36. ZHOU Zhanggui, DONG Guofeng. Energy performance contracting——A new mode of financing in SHP renovation project[J]. Small Hydro Power, 2009(4): 32-36. 本文引用 [1]

[5]	周艳聪，刘艳柳，顾军华. 小生境自适应遗传模拟退火智能组卷策略研究[J]. 小型微型计算机系统，2011, 32(2): 323-327. ZHOU Yancong, LIU Yanliu, GU Junhua. Research on test paper auto-generating based on niche adaptive genetic simulated annealing algorithms[J]. Journal of Chinese Computer Systems, 2011, 32(2): 323-327. 本文引用 [1]

[6]	游进军，纪昌明，付湘. 基于遗传算法的多目标问题求解方法[J]. 水利学报，2003, 34(7): 64-69. YOU Jinjun, JI Changing, FU Xiang. New method for solving multi objective problem based on genetic algorithm[J]. Journal of Hydraulic Engineering, 2003, 34(7): 64-69. 本文引用 [1]

[7]	袁晓辉，袁艳斌，王乘，等. 一种新型的自适应混沌遗传算法[J]. 电子学报，2006, 34(4): 708-712. YUAN Xiaohui, YUAN Yanbin, WANG Cheng, et al. A novel self-adaptive chaotic genetic algorithm[J]. Acta Electronica Sinica, 2006, 34(4): 708-712. 本文引用 [1]

[8]	邬峰，黄丽亚. 自适应模拟退火遗传算法的改进与应用[J]. 微型机与应用，2010, 29(9): 84-86. WU Feng, HUANG Liya. Improvement and application of an adaptive simulated annealing genetic algorithm[J]. Microcomputer & its Applications, 2010, 29(9): 84-86. 本文引用 [1]

[9]	李文武，吴稀西，黄进，等. 基于随机动态规划的混合式抽水蓄能电站水库中长期优化调度研究[J]. 电力系统保护与控制，2013, 41(9): 86-93. LI Wenwu, WU Xixi, HUANG Jin, et al. Mid-long term optimization of reservoir operation for hybrid pumped storage power station based on stochastic dynamic programming[J]. Relay, 2013, 41(9): 86-93. 本文引用 [1]

[10]	陈明，张靠社. 基于改进布谷鸟算法的风光储联合供电系统储能容量优化配置研究[J]. 电网与水力发电进展，2016(8): 141-146. CHEN Ming, ZHANG Kaoshe. Study on optimal storage capacity of energy storage capacity based on improved cuckoo algorithm[J]. Progress in Grid and Hydroelectric Power Generation, 2016(8): 141-146. 本文引用 [1]

[11]	郑立刚，周昊，王春林，等. 变尺度混沌蚁群算法在NO_x排放优化中的应用[J]. 中国电机工程学报，2008, 28(11): 18-23. ZHENG Ligang, ZHOU Hao, WANG Chunlin, et al. Application of scaleable chaotic ant colony algorithm in Nox emissions optimization[J]. Proceedings of the CSEE, 2008, 28(11): 18-23. 本文引用 [1]

[12]	王涛，王焕. 改进的自适应混沌差分进化算法[J]. 计算机系统应用，2013, 22(2): 138-141. WANG Tao, WANG Huan. Improved adaptive chaotic differential evolution algorithm[J]. Computer Systems & Applications, 2013, 22(2): 138-141. 本文引用 [1]

[13]

申建建，程春田，廖胜利，等. 基于模拟退火的粒子群算法在水电站水库优化调度中的应用[J]. 水力发电学报，2009, 28(3): 10-15.

SHEN

Jianjian

, CHENG

Chuntia

, LIAO

Shengli

, et al. Optimization of hydropower station operation by using particle swarm algorithm based on simulated annealing[J]. Journal of Hydroelectric Engineering, 2009, 28(3): 10-15.

本文引用 [1]

[14]	傅蓉，王维庆，何桂雄. 基于气象因子的BP神经网络风电场风速预测[J]. 可再生能源，2009, 27(5): 86-89. FU Rong, WANG Weiqing, HE Guixiong. The forecasting of wind speed in wind farm based on the meteorological factors with BP neural network[J]. Renewable Energy, 2009, 27(5): 86-89. 本文引用 [1]

[15]	乔海娟，张丛林，张军，等. 推进我国小水电发展的思考[J]. 海河水利，2015(2): 35-37. QIAO Haijuan, ZHANG Conglin, ZHANG Jun, et al. Analysis on promoting the development of small hydropower in China[J]. Haihe Water Resources, 2015(2): 35-37. 本文引用 [1]

[16]	吴学文，索丽生，王志坚. 水电站水库优化调度的改进混沌遗传算法[J]. 水利水电科技进展，2010, 30(2): 53-57. WU Xuewen, SUO Lisheng, WANG Zhijian. Improved chaotic genetic algorithm for optimal operation of hydropower reservoirs[J]. Advances in Science and Technology of Water Resources, 2010, 30(2): 53-57. 本文引用 [1]

[17]	李可，马孝义，符少华. 基于改进遗传算法的水电站优化调度模型与算法[J]. 水力发电，2010, 36(1): 92-96. LI Ke, MA Xiaoyi, FU Shaohua. Scheduling optimization model and algorithm based on improved genetic arithmetic for hydropower station[J]. Water Power, 2010, 36(1): 92-96. 本文引用 [1]