考虑电网剩余负荷预报的抽水蓄能电站短期优化调度OA
Optimizing short-term operation of pumped storage stations considering power grid residual load forecasting
随着风光等新能源大规模消纳并网,电力系统剩余负荷的强波动特性为抽水蓄能电站的短期运行调度带来严峻挑战.本文以湖南省电网及其黑麋峰抽水蓄能电站为研究对象,基于剩余负荷预报结果,构建了考虑多时段运行约束的抽水蓄能电站短期优化调度模型,分别以发电效益最大化和剩余负荷波动最小化为目标函数,考虑机组工况转换、水库水量平衡等复杂约束,并采用改进遗传算法进行高效求解.研究结果表明:基于剩余负荷预报,黑麋峰抽水蓄能电站经优化调度后平均日发电效益为 152.54 万元,相较于实际调度方案的发电效益提升了36%,且剩余负荷波动性降低了34%.研究成果可为高比例新能源接入下抽水蓄能电站经济运行与电网灵活调节提供关键技术支撑.
Large-scale integration of wind power and solar power has led to highly fluctuating residual loads on the power systems,posing a great challenge to the short-term operation of pumped storage stations.This study focuses on a real case of the Heimifeng pumped storage station that connects to the Hunan power grid in Central China.Based on previous studies of residual load forecasting,we develop a short-term model for optimal operation of the station,incorporating multi-period operational constraints.This model considers dual objectives-maximizing power generation benefit and minimizing residual load fluctuations;it is equipped with an improved genetic algorithm for efficient solution,applicable to the cases of complicated constraints-including operational mode transitions and reservoir water balance.Results demonstrate that with the forecasted typical residual load sequences used as input,the optimized operation achieves an average daily power generation benefit of 1.5254 million Yuan,or an increase of 36%relative to the existing real operation,while reducing residual load fluctuations by 34%.This study is practically useful for economical operation of a pumped storage station,and helps enhance grid flexibility under high penetration of renewable energy integration.
程名;周研来;韦溢龙;杨旭
武汉大学 水资源工程与调度全国重点实验室,武汉 430072武汉大学 水资源工程与调度全国重点实验室,武汉 430072中电建广西勘测设计研究院有限公司,南宁 530029中国长江电力股份有限公司,武汉 430014||智慧长江与水电科学湖北省重点实验室,武汉 430014
建筑与水利
抽水蓄能电站剩余负荷预报调度新能源消纳改进遗传算法
pumped storage power stationresidual loadforecast schedulingrenewable energy consumptionimproved genetic algorithm
《水力发电学报》 2026 (6)
1-11,11
国家重点研发计划项目(2024YFC3212700)中国长江电力股份有限公司项目(Z242502016)
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