Multicriteria Decision-Making Model of Reservoir Operation Considering Balanced Applicability in Past and Future: Application to the Three Gorges Reservoir
Date
2020Author
Wei Zhang
Xu Wang
Xiaohui Lei
Pan Liu
Xiaoran Yan
Maoyuan Feng
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Motivated by the emerging need for long-term and sustainable reservoir management, this paper developed a multicriteria decision-making (MCDM) model of reservoir operation with consideration of balanced applicability in both the past and the future. The proposed method consists of four modules: (1) the multiobjective optimization model of reservoir operation is built based on historical streamflow and then solved by the evolutionary multiobjective direct policy search (EMOPDS) with the nondominated sorting genetic algorithm-II (NSGA-II); (2) an evaluation criteria matrix is made considering the benefits and risks of the same objectives under future climate change; (3) objective interrelationships are qualitatively identified by visual analytics and quantitatively described by the structural equation model (SEM); and (4) an objective-based space coordinate system (SCS) is established to calculate the evaluation criteria weights based on SEM results, from which the optimum Pareto solution is determined by a decision-making function. The Three Gorges Reservoir (TGR) was selected as a case study, which simultaneously provides hydropower generation (pow), ecology (eco), and water storage (stor). Results indicate that eco negatively interacts with both pow and stor; however, pow is positively correlated with stor. Moreover, the design of the SCS-MCDM model can significantly improve benefits in both pow and stor and slightly sacrifice benefit in eco, compared with fuzzy optimum selection (FOS)-1 and FOS-2. Generally, the SCS-MCDM model outperforms FOS-1 and FOS-2 in terms of benefits for past and future and robustness in operation process under climate change. Therefore, the SCS-MCDM model facilitates decision makers to make a sustainable and balanced decision for reservoir management under uncertain climate change.