• Energy Research

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem.

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.5) emission scenario are adopted and utilized to predict the climate variables over a 30-year planning period. To solve this problem, an improved version of our recently proposed fuzzy multi-objective particle swarm optimization (f-MOPSO) algorithm, named f-MOPSO-II, is proposed. The f-MOPSO takes a novel approach to handle multi-objective nature of the optimization problems. In this approach, the common concept of "diversity" is replaced with "extremity," to choose the better guides of the search agents in the algorithm. The f-MOPSO-II is based on the f-MOPSO. However, it is aimed at simultaneously mitigating the f-MOPSO computational complexity and enhancing the quality of the final results presented by this algorithm. The results obtained by the f-MOPSO-II were then compared with those yielded by the popular non-dominated sorting genetic algorithm-II (NSGA-II). As the results suggest, the f-MOPSO-II is capable of simultaneously meeting the water demands and holding the reservoir storage sustainable, much better than the NSGA-II.