Abstract
This paper describes a successful adaptation of the gravitational search algorithm (GSA) and quasi-oppositional GSA (QOGSA) to solve short term hydrothermal scheduling (HTS) problem in power systems. Quasi-oppositional-based learning approach is integrated with GSA to efficiently control the local and global search, such that premature convergence is avoided and global solutions are achieved. Two test systems having non-convex solution spaces are used to validate the proposed QOGSA algorithm. The results obtained through QOGSA, GSA and those of the previous approaches are compared. The outcome of the comparisons reveals the effectiveness of the proposed QOGSA method in terms of solution quality, and computational speed.
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