梯级水利枢纽多尺度多目标联合优化调度研究
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摘要
面向人类社会发展和流域自身发展的需求,开展梯级水利枢纽多尺度多目标联合优化调度研究,并将研究成果应用于三峡-葛洲坝梯级水利枢纽实例中。主要研究内容和结论如下:
第一,引入聚类思想,提出聚类遗传算法,改进了选择算子和交叉算子;结合机组运行效率变化梯度改进了变异算子;实例表明:改进方式能降低早熟的概率和提高优化效率,适用于梯级水利枢纽优化调度问题。
第二,开展梯级水电站短期电力调度优化研究,构建“以水定电”和“以电定水”优化调度模型。提出了二级求解思路和机组组合、启停和运行效率同步实现策略。实例优化表明:模型和优化思路符合梯级水电站短期电力调度的需求。优化方案满足设定的目标和约束条件,可提高梯级的发电效率和效益。
第三,开展梯级水利枢纽长期优化调度研究。模拟总结了不同典型年下三峡-葛洲坝梯级的非汛期长期优化调度策略。构建了市场条件下以弃水风险和违约风险最小以及发电收益最优为多目标的梯级水电站长期风险调度模型,并采用概率分布和蒙特卡洛方法随机模拟月均入库流量预测误差和电力上报策略。最后,模拟得出了三葛梯级的非汛期风险调度方案。
第四,开展航运和电力调度耦合及智能快速决策研究。提出了航运条件和电站运行特性的评价方式,评价指标包括航运保证率、库水位符合率和平均库水位。结合典型模拟数据构建了三项指标的快速评估预测模型。最后,提出了耦合航运和电力调度多目标的梯级水利枢纽联合优化调度快速决策的实现策略。
第五,开展水质智能快速预测及其与电力调度的耦合策略研究。通过一维水流水质模拟评估了三峡枢纽调峰运行对库区水流和水质的影响。构建了以污染物浓度的天平均变化速率为目标的水质智能快速预测BP神经网络模型。模型预测精度较高,满足实际需求。最后,提出了耦合水质改善和电力调度多目标的梯级水利枢纽联合优化调度的实现策略。
第六,综合前五部分研究内容,开展三峡-葛洲坝梯级水利枢纽多尺度多目标联合优化调度研究。提出并构建了相应的联合优化调度模型及其优化求解策略。最后,通过实例模拟得出了三峡-葛洲坝梯级水利枢纽非汛期的多目标联合优化调度策略。
Oriented to demands of human society and drainage basin development, researchon multi-scale and multi-objective combined optimal dispatching of cascadedhydropower junctions is developed in this paper. And all the results are fully appliedto Three Gorges and Gezhouba cascaded hydropower junctions. Main research workandconclusionsareasfollows:
Firstly,cluster genetic algorithm is put forwardto improvingselectionoperatorandcrossover operator. Mutation operator is also improved by bringing in changinggradient of units’operation efficiency. Results of real case show that theimprovements can reduce premature possibility, and optimization is also faster andwithhigherefficiency.
Secondly, short-term optimal hydropower dispatching for cascaded hydropowerstations is developed with two different optimization models, which considerefficiency, stability and generation benefit as multi aims. Two-stage optimizationstrategyand synchronous achievement strategyis also proposedtoreducecomplexity.Optimal solutions show that constructed models and optimization strategies ispragmatic. Besides above, dispatching solutions realize all the aims and constraintswithmoregenerationbenefitandhigherefficiency.
Thirdly, scheduling solutions of different typical years in drawdown season areconcluded after optimization for Three Gorges and Gezhouba cascaded hydropowerjunctions. Oriented to power market, long-term risk scheduling model is proposedwith min waste water risk, min default risk and max generation benefit as multi aims.Forecasting error of monthly inflow and electricity bidding strategy is simulated byprobabilitydistribution and Monte-Carlo method. Finally, risk scheduling solutions indrawdown season are optimized for Three Gorges and Gezhouba cascadedhydropowerjunctions.
Fourthly, couple strategy and intelligent fast decision for navigation andhydropower dispatching is developed. Three coefficients are put forward to evaluatenavigation condition or operation characteristics, which are navigation guaranty rate,level guaranty rate and average level. Fast forecasting model for them is alsoconstructed based on typical simulations. Finally, realization strategies for intelligentfast decision with navigation and hydropower dispatching as multi aims are proposedforcascadedhydropowerjunctions.
Fifthly, intelligent fast forecasting of water quality and couple strategy for waterqualityand hydropower dispatchingis developed. Waterflow andwater qualityunderdifferent scheduling modes of Three Gorges hydropower station is simulated by onedimension water and water quality model. Taken daily average change rate ofcontamination concentration as evaluation parameter, intelligent fast forecasting BPneuron network model of water quality is constructed with high precision. Finally,realization strategies for combined optimal dispatching with water qualityimprovement and hydropower dispatching as multi aims are proposed for cascadedhydropowerjunctions.
Sixthly, based on the research work of five parts above, multi-scale andmulti-objective combined optimal dispatching model and optimization strategies areproposed and constructed for Three Gorges and Gezhouba cascaded hydropowerjunctions. Finally, dispatching solutions in drawdown season are optimized andconcludedforitafterrealsimulation.
第一,引入聚类思想,提出聚类遗传算法,改进了选择算子和交叉算子;结合机组运行效率变化梯度改进了变异算子;实例表明:改进方式能降低早熟的概率和提高优化效率,适用于梯级水利枢纽优化调度问题。
第二,开展梯级水电站短期电力调度优化研究,构建“以水定电”和“以电定水”优化调度模型。提出了二级求解思路和机组组合、启停和运行效率同步实现策略。实例优化表明:模型和优化思路符合梯级水电站短期电力调度的需求。优化方案满足设定的目标和约束条件,可提高梯级的发电效率和效益。
第三,开展梯级水利枢纽长期优化调度研究。模拟总结了不同典型年下三峡-葛洲坝梯级的非汛期长期优化调度策略。构建了市场条件下以弃水风险和违约风险最小以及发电收益最优为多目标的梯级水电站长期风险调度模型,并采用概率分布和蒙特卡洛方法随机模拟月均入库流量预测误差和电力上报策略。最后,模拟得出了三葛梯级的非汛期风险调度方案。
第四,开展航运和电力调度耦合及智能快速决策研究。提出了航运条件和电站运行特性的评价方式,评价指标包括航运保证率、库水位符合率和平均库水位。结合典型模拟数据构建了三项指标的快速评估预测模型。最后,提出了耦合航运和电力调度多目标的梯级水利枢纽联合优化调度快速决策的实现策略。
第五,开展水质智能快速预测及其与电力调度的耦合策略研究。通过一维水流水质模拟评估了三峡枢纽调峰运行对库区水流和水质的影响。构建了以污染物浓度的天平均变化速率为目标的水质智能快速预测BP神经网络模型。模型预测精度较高,满足实际需求。最后,提出了耦合水质改善和电力调度多目标的梯级水利枢纽联合优化调度的实现策略。
第六,综合前五部分研究内容,开展三峡-葛洲坝梯级水利枢纽多尺度多目标联合优化调度研究。提出并构建了相应的联合优化调度模型及其优化求解策略。最后,通过实例模拟得出了三峡-葛洲坝梯级水利枢纽非汛期的多目标联合优化调度策略。
Oriented to demands of human society and drainage basin development, researchon multi-scale and multi-objective combined optimal dispatching of cascadedhydropower junctions is developed in this paper. And all the results are fully appliedto Three Gorges and Gezhouba cascaded hydropower junctions. Main research workandconclusionsareasfollows:
Firstly,cluster genetic algorithm is put forwardto improvingselectionoperatorandcrossover operator. Mutation operator is also improved by bringing in changinggradient of units’operation efficiency. Results of real case show that theimprovements can reduce premature possibility, and optimization is also faster andwithhigherefficiency.
Secondly, short-term optimal hydropower dispatching for cascaded hydropowerstations is developed with two different optimization models, which considerefficiency, stability and generation benefit as multi aims. Two-stage optimizationstrategyand synchronous achievement strategyis also proposedtoreducecomplexity.Optimal solutions show that constructed models and optimization strategies ispragmatic. Besides above, dispatching solutions realize all the aims and constraintswithmoregenerationbenefitandhigherefficiency.
Thirdly, scheduling solutions of different typical years in drawdown season areconcluded after optimization for Three Gorges and Gezhouba cascaded hydropowerjunctions. Oriented to power market, long-term risk scheduling model is proposedwith min waste water risk, min default risk and max generation benefit as multi aims.Forecasting error of monthly inflow and electricity bidding strategy is simulated byprobabilitydistribution and Monte-Carlo method. Finally, risk scheduling solutions indrawdown season are optimized for Three Gorges and Gezhouba cascadedhydropowerjunctions.
Fourthly, couple strategy and intelligent fast decision for navigation andhydropower dispatching is developed. Three coefficients are put forward to evaluatenavigation condition or operation characteristics, which are navigation guaranty rate,level guaranty rate and average level. Fast forecasting model for them is alsoconstructed based on typical simulations. Finally, realization strategies for intelligentfast decision with navigation and hydropower dispatching as multi aims are proposedforcascadedhydropowerjunctions.
Fifthly, intelligent fast forecasting of water quality and couple strategy for waterqualityand hydropower dispatchingis developed. Waterflow andwater qualityunderdifferent scheduling modes of Three Gorges hydropower station is simulated by onedimension water and water quality model. Taken daily average change rate ofcontamination concentration as evaluation parameter, intelligent fast forecasting BPneuron network model of water quality is constructed with high precision. Finally,realization strategies for combined optimal dispatching with water qualityimprovement and hydropower dispatching as multi aims are proposed for cascadedhydropowerjunctions.
Sixthly, based on the research work of five parts above, multi-scale andmulti-objective combined optimal dispatching model and optimization strategies areproposed and constructed for Three Gorges and Gezhouba cascaded hydropowerjunctions. Finally, dispatching solutions in drawdown season are optimized andconcludedforitafterrealsimulation.
引文
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