REVIEW OF RESERVOIR OPERATION

Abstract

Reservoir operation occupies an important place in the utilization of water resources. Large-scale reservoirs play an essential role in water resource management for agricultural irrigation, water supply, and flood control. However, we need robust reservoir operation systems under both normal flow and extreme flow conditions. The use of models and decision tools for real-world reservoir operations is limited due to the gap between the models/tools and real-world practices, the tedious amount of work in case-by-case developments, and the computational difficulty of running complex numerical models. In reservoir operation, an appropriate methodology for deriving reservoir operating rules should be selected, and operating rules should then be formulated. Various algorithms and techniques are used to optimize the operation of existing multipurpose reservoirs and to derive reservoir operating rules for optimal reservoir operations. Parameter uncertainty inherent in reservoir operation affects operation model robustness and has been considered in conventional operations focusing on improving hydropower generation. With more attention being paid to ecological environmental protection recently, reversible ecosystem protection requires environmental flow (e-flow) management to sustain a near-natural flow regime. Whether there is e-flow management in reservoir operation has an impact on the uncertainty of reservoir operation. Optimizing storage reservoir operations aims to ensure that all planned reservoir objectives are met without compromising those ecological water requirements. It takes into account a variety of objectives and variables, including cost and revenue considerations of water allocation for various socioeconomic uses. Various computer simulation models can be used for optimization. The models use algorithms to calculate the optimal balance between water release and reservoir storage volumes. This era is the era of science and technology. There are many algorithms for the optimal reservoir, including the genetic algorithm (GA), the honey-bee mating optimization (HBMO) algorithm, the artificial fish swarm algorithm (AFSA), the particle swarm optimization algorithm (PSOA) (hybrid approach), the Jaya algorithm, and a multiobjective evolutionary algorithm (MOEA). The results obtained using the proposed evolutionary algorithm are able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them.