Optimum design and operation of a hydropower reservoir considering uncertainty of inflow

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.