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作者机构:Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Univ Waterloo Dept Syst Design Engn Waterloo ON Canada
出 版 物:《WATER RESOURCES RESEARCH》 (Water Resour. Res.)
年 卷 期:2025年第61卷第6期
核心收录:
学科分类:0710[理学-生物学] 0830[工学-环境科学与工程(可授工学、理学、农学学位)] 08[工学] 081501[工学-水文学及水资源] 0815[工学-水利工程]
基 金:Natural Sciences and Engineering Research Council of Canada NSERC Discovery grant
主 题:multireservoir hydropower operations stochastic dynamic programming implicit stochastic programming risk and efficient frontier
摘 要:Long-term multireservoir operations optimization is challenging for existing optimization methods such as stochastic dynamic programming (SDP) and implicit stochastic programming (ISP) suffering from excessive computing time requirements. More difficult is to tackle a risk-based optimization problem and provide an efficient frontier of the objective function for multireservoir systems. The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method suitable for multireservoir operations optimization which faces no curse of dimensionality of SDP and has no need for scenario generations of ISP, thus is extremely fast. Earlier implementations have developed expressions for mean and variance of storages and releases, including deficits and surpluses, to estimate fairly accurate values of the linear and quadratic objective functions when compared with other well-known methods. This paper introduces analytical derivations of hydropower equations to be used in the recent extension of the FP method and applies it to a long-term operations optimization problem of a three-reservoir system in Iran. The objective function is to maximize the expected value of the annual energy, which is a multiplicative nonlinear function of both releases and storage levels. The computational results from simulations for the 60 years of available inflow data for the chosen multireservoir system using the policies derived by the FP, ISP, and SDP methods were compared. The solution qualities were nearly the same, but the FP method has tremendous speedups over the other methods. Secondly, expressions for the variances of monthly energy productions were derived to compute efficient frontier for risk-return tradeoffs of annual energy to guide decision makers.