Abstract
This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cascade of 4 hydro and 3 thermal units whose valve-point loading effects are also taken into account. Furthermore, statistical outcomes of the conventional heuristic approaches available in the literature are compared with the proposed GWO and OGWO approaches, and these methods give moderately better operational fuel cost and emission in less computational time.
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