An oppositional learning based gravitational search algorithm for short term hydrothermal scheduling

Abstract

This paper presents an oppositional based gravitational search algorithm (OGSA) for solving the short term hydrothermal scheduling (STHTS) problem. The STHTS problem involves the optimization of nonlinear constrained objective function by taking into consideration of multireservoir cascaded nature hydro plants, water transportation delay between cascaded reservoirs and scheduled time linkages, variable system active load balance, water discharge and reservoir storage limits, initial and final reservoir storage limits, reservoir flow balance and operating limits of hydro and thermal plants. A stochastic search algorithm known as gravitational search algorithm (GSA) inspired from the law of gravity and mass interactions is used to solve this complex constrained STHTS problem. In order to improve the convergence rate of GSA, the opposite numbers are utilized in the evolution process of GSA. Finally, the proposed oppositional gravitational search algorithm (OGSA) approach is evaluated on two test systems, one consisting of four hydro plants and an equivalent thermal plant and the other one with nine cascaded hydro and three thermal plants. The results obtained with the proposed approach give better solution in terms of less production cost, execution time and better convergence characteristics while comparing with the results of other methods reported in the literature.