文章摘要
李 佳, 曲 田, 朱艳军, 吕俞锡, 闻 昕.大型梯级水电站枯水期多模式优化调度模型研究Journal of Water Resources and Water Engineering[J].,2024,35(1):124-132
大型梯级水电站枯水期多模式优化调度模型研究
Multi-mode scheduling of large-scale cascade hydropower stations in dry season
  
DOI:10.11705/j.issn.1672-643X.2024.01.15
中文关键词: 梯级水电站  枯水期  多模式优化调度  消落风险
英文关键词: cascade hydropower stations  dry season  multi-mode optimal scheduling  water level drop risk
基金项目:国家重点研发计划项目(2019YFE0105200); 国电大渡河流域水电开发有限公司科技创新项目(820074316)
Author NameAffiliation
LI Jia1, QU Tian1, ZHU Yanjun1, L Yuxi2, WEN Xin2 (1.国能大渡河流域水电开发有限公司 生产指挥中心 四川 成都 610041 2.河海大学 水利水电学院 江苏 南京 210098) 
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中文摘要:
      针对大型梯级水电站枯水期调度面临的市场环境复杂、枯汛转换期来水形势多变等难题,提出了梯级水电站调度决策多模式自适应匹配方法,分别建立“无(消落)压力环境”模式和“有(消落)压力环境”模式下的优化调度模型,根据预报信息自动选择和灵活切换调度模式和目标,生成各电站水位动态控制和灵活调整策略。结果表明:“无(消落)压力环境”模式下可使流域发电量增加2.57%;“有(消落)压力环境”的“均匀突破”模式和“集中突破”模式下分别可增加发电量4.59%、5.32%,前者面临无法消落到位的风险更小,后者发电效益更大。该模型可有效降低枯水期消落和发电风险,提升流域整体发电效益,对于不同来水、发电等工况均表现出较好的适应能力和优化效果。
英文摘要:
      The regulation of large-scale cascade hydropower stations in dry season are confronted with complex market environment and changeable inflow situations during the dry-wet transition period. To address this issue, a multi-mode adaptive matching method for the operation decision of cascade hydropower stations is established. Under “no (drop) pressure environment” mode and “with (drop) pressure environment” mode, we constructed an optimal scheduling model, which can select and switch scheduling mode and target automatically and flexibly according to the forecast information, and generate dynamic control and flexible adjustment strategies for the water level at each hydropower station. The simulation results show that under the mode of “no (drop) pressure environment”, the power generation increased by 2.57%; under the mode of “with (drop) pressure environment”, the power generation increased by 4.59% and 5.32% under the approaches of “uniform breakthrough” and “centralized breakthrough”, respectively. The former mode faces less risk of failure to reduce water level in place, while the latter generates greater power benefits. The model can effectively reduce the risk of water level drop and power generation in dry season, as well as improve the overall power generation benefit of the basin. It has excellent adaptability and optimization effect for different inflows, power generation and other working conditions.
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