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Application of hybrid heuristic optimization algorithms for solving optimal hydrothermal system operation

This paper provides a thorough comparative assessment of the capabilities of three hybrid metaheuristic algorithms for solving the optimal hydrothermal system operation (OHSO) problem. Among the selected algorithms are Differential Evolution with Adaptive Crossover Operator (DE-ACO), Linearized Biogeography-based Optimization (LBBO), and Hybrid Median-Variance Mapping Optimization (MVMO-SH). Numerical tests are performed on a benchmark system composed by four cascaded hydro plants and an equivalent thermal plant. Performance comparisons include convergence speed, achieved optimum solutions, computing effort, and closeness with results obtained through classical non-linear programming optimization.