A novel approach for economic dispatch of hydrothermal system via gravitational search algorithm

The daily economic dispatching of hydrothermal system (DHS), which is a large-scale dynamic nonlinear constrained optimization problem, plays an important role in economic operation of electric power systems. This paper proposes a novel enhanced gravitational search algorithm (EGSA) to solve DHS problem. In the proposed method, the improvements mainly include three aspects. Firstly, particle swarm optimization (PSO) that acts complementary is integrated into gravitational search algorithm for update of agent's velocity. Secondly, heuristic search strategies based random selected dependent discharge of hydro plants and average full-load cost priority list of thermal units are adopted to deal with equality constraints of DHS problem. Thirdly, feasibility-based selection comparison techniques are devised to effectively handle inequality constraints in EGSA, which do not require penalty factors or extra parameters and can guide the agent to the feasible region quickly. The feasibility and effectiveness of the proposed EGSA method is verified by a hydrothermal test system and the simulation results are compared with those of differential evolution, PSO, genetic algorithm, classical evolutionary programming, fast evolutionary programming, and improved fast evolutionary programming algorithm. From the results, it clearly shows that the proposed method gives better quality solutions than other methods.