Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling

This paper presents a new approach for solving the short-term hydrothermal scheduling (STHTS) problem using a disruption operator in an oppositional gravitational search algorithm. The nonlinear and non-convex nature of the STHTS problem coupled with the cascading nature of reservoirs, water transport delays and scheduling time linkages makes the solution of this optimization problem quite difficult using the conventional optimization methods. Here, an opposition-based learning concept is introduced in a gravitational search algorithm to improve the quality of the current population towards global optimal solutions and a disruption operator is integrated to accelerate the convergence of solutions. This method is evaluated on two test systems consisting of four hydro and an equivalent thermal plant and four hydro and three thermal plants. The detailed statistical results prove that the proposed approach performs better in terms of production cost and smooth convergence characteristics when compared with other recently reported methods in the literature.