电力市场环境下基于电价预测的水库优化调度研究
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摘要
随着电力市场的开放和分时电价制度的实施,水电站水库在电力系统中的作用显得日益重要,以往对水电站水库优化调度单纯追求发电量最大的优化准则已不能适应新时期的发展要求。研究分时电价条件下水电站水库优化调度问题,对于充分利用水能资源,提高发电厂的经济效益,具有重要的理论意义和实用价值。
本论文在总结以往相关领域研究成果的基础上,针对在电力市场环境下由于电价水平的差异引起的相同发电量并不易意味着相同发电效益、传统的以发电量最大为目标的水库优化调度策略已不能给水电站带来最大的发电效益的问题,建立了发电量最大发电效益最大模型并进行了求解,实例分析表明发电效益最大模型较发电量最大模型能带来更大的经济效益。论文的主要内容包括：
(1)在阅读大量国内外文献的基础上,综合评述了国内外水电站优化调度理论、方法及应用现状,并对电力市场及电价相关领域知识进行了阐述。
(2)在综述了负荷预测研究的意义和方法的基础上对灰色理论进行了论述,分析了灰色理论应用于预测的原理,重点介绍了GM(1,1)模型、GM(1,1)改进模型和残差模型,并对美国加州电力市场用以上模型建模,对2000年9月14日至9月20日的负荷做了预测。结果表明,GM(1,1)改进模型和残差模型都比GM(1,1)模型不同程度的提高了预测精度。
(3)利用BP神经网络对边际电价进行了预测,采用相似搜索对输入量进行选取,并用平均百分比误差和最大百分比误差两个参数对预测精度进行了分析,之后用相关性分析技术对输入量的选取进行了改进,结果显示利用相关性分析技术对预测模型输入量进行选取后预测效果有了较为明显的改善,尤其是对周末边际电价的预测精度有很大程度的提高,最大百分比误差由49.73%减小到29.92%,平均百分比误差也由14.34%减小到12.41%。这说明利用相关性分析技术对输入因子的选取进行改进能较大程度的提高预测精度。
(4)系统分析了目前我国水库优化调度的意义、规则和优化调度准则,以珠江某水电站水库为例,分别建立了发电量最大及发电效益最大两种模型,根据不同时段电价的差异,综合考虑了水库蓄水位、机组出力限制、电厂下泄流量、机组特性曲线、库容特性曲线等众多约束条件,利用POA算法优化理论求解优化模型,结果表明以发电效益最大为目标,日经济收入增加了3.8万元,发电效益提高了3.0939%。
Along with the the opening of power market and the practice of time-varying electricity price policy, the role of reservoirs in the electric power system becomes more and more important, optimization criteria of maximum of power output is not so practical now. In order to fully utilize water resources and promote economic benefits of hydroelectric plants, it is of great theoretical and practical important to research on reservoir optimal operation based on time-varying electricity price.
Based on summarizing the existing study results in related domain, in view of the same generating capacity is not easy to have the same means of power generation efficiency due to different level of electricity price in the power market, traditional power generation in order to target the largest reservoir of the optimal scheduling strategy has not brought about the largest power generation efficiency to the hydropower station, power generation largest and the largest power generation efficiency model is established and solved. Analysis result shows that the most effective model of power generation capacity can bring greater economic benefits than the largest model.Major content is as follows:
(1) Based on reading a lot of domestic and foreign literature, the merits and short comings of the theories and methods in the optimal generation scheduling of hydropower plants are stated and the futher research orientation are pointed out.
(2) Discussed the gray theory on the basis of overview of significance and research methods of load forecast, and analysisd the principle of gray theory applies to prediction. Focused on GM (1,1) model, improved GM (1,1) model and residual model, and California electricity market is modeled in the United States, forecasted the load from sept.14 to sept.20 in 2000. Results shows that, improved GM (1,1) model and residual model improved the forecast accuracy in varying degrees than GM (1,1) model.
(3) Forecasted marginal price with BP neural network, use similar search to select input, analysis forecast accuracy with average percentage of error and the largest percentage of error, then improved input using correlation analysis. Results show that, using correlation analysis to select input, forecast effect is more marked improvement, the largest percentage of error reduced to 29.92 percent from 49.73 percent, average percentage of error reduced to 12.41 percent from 14.34 percent. This shows that the using of relevance technical analysis of input to improve the selection can improve forecast accuracy.
(4) Analysis of significance, rules and guidelines for optimal operation of the current reservoir in China, the power generation largest and power efficiency largest two models is established, as an example to a hydroelectric dam in the Pearl River, according to the different price, considering many constraints such as reservoir water level, unit output limit, power plant discharge flow, unit curve, storage capacity curve and so on, using POA algorithm optimization theory to solve model, and the results shows that power efficiency largest as target, Daily income increased by 38,000 yuan, the electricity generation benefit enhanced 3.0939%.
本论文在总结以往相关领域研究成果的基础上,针对在电力市场环境下由于电价水平的差异引起的相同发电量并不易意味着相同发电效益、传统的以发电量最大为目标的水库优化调度策略已不能给水电站带来最大的发电效益的问题,建立了发电量最大发电效益最大模型并进行了求解,实例分析表明发电效益最大模型较发电量最大模型能带来更大的经济效益。论文的主要内容包括：
(1)在阅读大量国内外文献的基础上,综合评述了国内外水电站优化调度理论、方法及应用现状,并对电力市场及电价相关领域知识进行了阐述。
(2)在综述了负荷预测研究的意义和方法的基础上对灰色理论进行了论述,分析了灰色理论应用于预测的原理,重点介绍了GM(1,1)模型、GM(1,1)改进模型和残差模型,并对美国加州电力市场用以上模型建模,对2000年9月14日至9月20日的负荷做了预测。结果表明,GM(1,1)改进模型和残差模型都比GM(1,1)模型不同程度的提高了预测精度。
(3)利用BP神经网络对边际电价进行了预测,采用相似搜索对输入量进行选取,并用平均百分比误差和最大百分比误差两个参数对预测精度进行了分析,之后用相关性分析技术对输入量的选取进行了改进,结果显示利用相关性分析技术对预测模型输入量进行选取后预测效果有了较为明显的改善,尤其是对周末边际电价的预测精度有很大程度的提高,最大百分比误差由49.73%减小到29.92%,平均百分比误差也由14.34%减小到12.41%。这说明利用相关性分析技术对输入因子的选取进行改进能较大程度的提高预测精度。
(4)系统分析了目前我国水库优化调度的意义、规则和优化调度准则,以珠江某水电站水库为例,分别建立了发电量最大及发电效益最大两种模型,根据不同时段电价的差异,综合考虑了水库蓄水位、机组出力限制、电厂下泄流量、机组特性曲线、库容特性曲线等众多约束条件,利用POA算法优化理论求解优化模型,结果表明以发电效益最大为目标,日经济收入增加了3.8万元,发电效益提高了3.0939%。
Along with the the opening of power market and the practice of time-varying electricity price policy, the role of reservoirs in the electric power system becomes more and more important, optimization criteria of maximum of power output is not so practical now. In order to fully utilize water resources and promote economic benefits of hydroelectric plants, it is of great theoretical and practical important to research on reservoir optimal operation based on time-varying electricity price.
Based on summarizing the existing study results in related domain, in view of the same generating capacity is not easy to have the same means of power generation efficiency due to different level of electricity price in the power market, traditional power generation in order to target the largest reservoir of the optimal scheduling strategy has not brought about the largest power generation efficiency to the hydropower station, power generation largest and the largest power generation efficiency model is established and solved. Analysis result shows that the most effective model of power generation capacity can bring greater economic benefits than the largest model.Major content is as follows:
(1) Based on reading a lot of domestic and foreign literature, the merits and short comings of the theories and methods in the optimal generation scheduling of hydropower plants are stated and the futher research orientation are pointed out.
(2) Discussed the gray theory on the basis of overview of significance and research methods of load forecast, and analysisd the principle of gray theory applies to prediction. Focused on GM (1,1) model, improved GM (1,1) model and residual model, and California electricity market is modeled in the United States, forecasted the load from sept.14 to sept.20 in 2000. Results shows that, improved GM (1,1) model and residual model improved the forecast accuracy in varying degrees than GM (1,1) model.
(3) Forecasted marginal price with BP neural network, use similar search to select input, analysis forecast accuracy with average percentage of error and the largest percentage of error, then improved input using correlation analysis. Results show that, using correlation analysis to select input, forecast effect is more marked improvement, the largest percentage of error reduced to 29.92 percent from 49.73 percent, average percentage of error reduced to 12.41 percent from 14.34 percent. This shows that the using of relevance technical analysis of input to improve the selection can improve forecast accuracy.
(4) Analysis of significance, rules and guidelines for optimal operation of the current reservoir in China, the power generation largest and power efficiency largest two models is established, as an example to a hydroelectric dam in the Pearl River, according to the different price, considering many constraints such as reservoir water level, unit output limit, power plant discharge flow, unit curve, storage capacity curve and so on, using POA algorithm optimization theory to solve model, and the results shows that power efficiency largest as target, Daily income increased by 38,000 yuan, the electricity generation benefit enhanced 3.0939%.
引文
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