Publication: Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm
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Date
2021
Authors
Chong K.L.
Lai S.H.
Ahmed A.N.
Wan Jaafar W.Z.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
The production and use of energy from hydropower generation play a vital role in the economy. Besides, the presence of uncertainty further increases the complexity in optimizing the reservoir operation. A synthetic streamflow generation based on historical inflow records was employed using the Thomas�Fiering model for handling the uncertainty and variability of reservoir inflows. However, under the circumstances of water deficiency, the hydropower output is significantly reduced. In this study, an investigation of a parameter free Jaya algorithm as an optimization method for reservoir operation was carried out. When deriving the optimal operational rule a hedging strategy is introduced to attenuate the impact of reduced water supply. This strategy can effectively counterbalance the lack of water supply with reservoir storage requirements. The higher amount of hydropower generated by the proposed algorithm than the other algorithms used in this study, such as genetic algorithm (GA), the ant colony algorithm (ACO), the bat algorithm (BA), the particle swarm optimization (PSO) algorithm, chicken swarm optimization (CSO) algorithm, grasshopper optimization algorithm (GOA), equilibrium optimizer (EO) and firefly algorithm (FA), has shown its efficiency in the reservoir system. Several reservoir performance indices, such as total hydropower generation, reliability, and resilience, were used to access the proposed algorithm and other algorithms efficiency � 2021 Elsevier B.V.
Description
Ant colony optimization; Hydroelectric power; Hydroelectric power plants; Investments; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Water supply; Ant colony algorithms; Hydro-power generation; Hydropower reservoirs; Optimization algorithms; Particle swarm optimization algorithm; Reservoir performance; Streamflow generations; Uncertainty and variability; Genetic algorithms