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From Probabilistic Seasonal Streamflow Forecasts to Optimal Reservoir Operations: A Stochastic Programming Approach

机译：从概率季节性流流程预测到最佳储层操作：随机编程方法

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We investigate the potential use of seasonal streamflow forecasts for the real-time operation of Angat reservoir (Philippines). The system is characterized by a strongintra- and inter-annual variability in the imflow process，which is further amplified by the ElNinio Southern Oscillation(ENSO). We bank on the relationship between ENSO indices andlocal hydro-climatological processes to issue probabilistic streamflow forecasts(with a 3-monthforecast horizon) and then integrate them within a Multistage Stochastic Programming (MSP)approach. The rolling-horizon, forecast-informed scheme is adopted for the period 1968-2014 andbenchmarked against deterministic optimization solutions with perfect forecasts,climatology,and mean forecasts. We also compare its performance with the current operating rules， andthe operating rules obtained by solving a Stochastic Dynamic Programming problem. Resultsshow that the MSP approach can help reduce the severity of failures during prolonged droughtscaused by ENSO.
机译：我们调查季节性流流量预测的潜在使用，以获得Angat储层的实时运行（菲律宾）。该系统的特征在于IMFlow过程中的强度和年间可变性，其通过Elninio Southern振荡（ENSO）进一步放大。我们银行对ENSO指数与流域水力 - 气候流程之间的关系，以发布概率流流量预测（具有3个月平衡地平线），然后将它们集成在多级随机编程（MSP）方法中。在1968 - 2014年期间采用了滚动地平线，预测的计划，并采用了具有完美预测，气候学和平均预测的确定性优化解决方案。我们还通过当前的操作规则进行比较其性能，通过解决随机动态规划问题而获得的操作规则。结果表明，MSP方法有助于减少enso长时间滴灌期间失败的严重程度。

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