考虑发电与弃水风险的梯级水电站水库风险调度模型

Risk dispatching model of cascade hydropower station reservoirs considering power generation and water abandonment risks

为量化梯级水电站水库调度过程中的风险形势,提出了预报信息驱动的梯级水电站水库风险调度模型。基于长短期记忆(LSTM)神经网络建立风险形势预测模型,通过构造预报入库径流、水库运行工况、调度决策与出力破坏程度和弃水量之间的映射关系,动态推演复杂工况下梯级水电站水库调度风险;建立基于风险形势的梯级水电站水库调度模型,提出发电不足风险预控模式和弃水风险预控模式。选取雅砻江流域下游锦屏一级和二滩水库组成的梯级水电站水库作为实例研究,结果表明:与常规发电量最大模型相比,风险调度模型通过提前降低出力来补偿未来出力破坏时段的发电量,锦屏一级和二滩水电站分别减小出力破坏程度46%、63%,通过提前增加发电流量预留库容来缓解蓄水期后续时段的弃水量,锦屏一级和二滩水电站分别降低弃水流量4.8%、5.4%;风险形势预测模型能够将径流不确定性嵌入风险调度模型中,通过风险预控决策动态响应水电站水库调度运行状态和径流形势变化,在保证梯级发电量的同时有效降低调度风险。

To quantify the risk situation during the operation of cascade hydropower station reservoirs,a risk operation model driven by forecast information was proposed.Based on long-short term memory(LSTM)neural network,a risk situation prediction model was established.By constructing a mapping relationship between predicted inflow runoff,reservoir operating conditions,scheduling decisions,output damage degree,and abandoned water volume,the risk of reservoir scheduling in cascade hydropower stations under complex operating conditions was dynamically deduced.A risksituation based scheduling model for cascade hydropower station reservoirs was established,and risk pre control models for insufficient power generation and water abandonment were proposed.A case study was conducted on a cascade hydropower station consisting of the Jinping I Reservoir and the Ertan Reservoir in the lower reaches of the Yalong River Basin.The results show that compared to the conventional maximum power generation model,the risk scheduling model compensates for future output shortage periods by reducing a certain amount of output in advance,reducing the depth of output shortage of the Jinping I Reservoir and Ertan Reservoir by 46%and 63%,respectively.Additionally,it increases power generation discharge in advance to reserve storage capacity,thus alleviating the water surplus in the subsequent periods in the wet season,leading to a reduction of total water surplus of the Jinping I Reservoir and Ertan Reservoir by 4.8%and 5.4%,respectively.The risk situation prediction model can incorporate the uncertainty of inflow into the risk scheduling model,enabling dynamic response to the operational state of the hydropower station reservoir and changes in inflow situation through risk control decisions.