Abstract
Abstract Due to the large number of variables and nonlinear relations, hydropower plant design and operation optimization problems belong to the Non-polynomial hard class of problems. In this study, optimum design and operation of a hydropower reservoir is compared in two cases using deterministic and stochastic inflows by two meta-heuristic algorithms. Particle swarm optimization (PSO) and cuckoo optimization algorithm (COA) are applied under two conditions of using the historical inflow time series as a deterministic approach and the eigenvector-based synthetic generations as a stochastic approach for optimum design and operation of the Bakhtiari hydropower plant in Iran. The problem is solved in two states of finding the optimum values for the reservoir and power plant capacities (as the design decision variables) with known standard operation policy (SOP) and optimum values for the capacities and the reservoir releases variables (as the design and operating variables). Results obtained by the models indicate that the role of operation optimization is negligible as the SOP used in the design models led to near optimum solutions. Considering uncertainty in the reservoir inflows resulted in an increase of the installation capacity and consequently the energy production. In addition, PSO demonstrated more efficiency compared to COA in dealing with the proposed optimization problem that has a complex feasible search space.
Highlights
Given the increase in population and the limitation and nonuniform distribution of water resources, as well as the overuse of these limited resources, the need for optimal management and utilization of the existing resources has become more evident
For generation of the synthetic time series to see the effect of uncertainty in the reservoir inflow on the optimum design and operation of the hydropower plant, a normal distribution function is fitted on the monthly historical data
Four models are developed which, in the first two, the design variables are searched by the optimization algorithms and in the two the operation variables are added to the set of decision variables
Summary
Given the increase in population and the limitation and nonuniform distribution of water resources, as well as the overuse of these limited resources, the need for optimal management and utilization of the existing resources has become more evident. Optimization of the design and operation of hydroelectric reservoirs through classic optimization methods is associated with difficulties such as nonlinearity and non-convexity of the problem, as well as the procedure of determining the stochastic constraints related to the reliability of energy demands. In this regard, the simulation-based meta-heuristic search algorithms are suitable alternatives (Mousavi & Shourian ). The main objective of this study is to optimize the design and operation of a hydropower reservoir considering the uncertainty of the inflow to the reservoir as the one of the most important factors affecting the rate of energy generation
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