Exponential hybrid mutation differential evolution for economic dispatch of large-scale power systems considering valve-point effects

Economic dispatch (ED) is a key foundational issue for optimal power system operation and scheduling control. It is a complex multi-constraint optimization problem, especially taking into account the valve-point effects of thermal power generators. As the power system continues to grow in size, the ED problem becomes more sophisticated and the solution space will have more local extrema, which makes the solution methods more prone to premature convergence. Thus, the existing methods encounter difficulties in achieving satisfactory solutions. To address this issue, this study presents an exponential hybrid mutation differential evolution (EHMDE), which utilizes two improved strategies including exponential population size reduction and hybrid mutation operation to adaptively equilibrate exploitation and exploration during the iteration process. The former strategy can maintain population diversity to avoid getting stuck in a local optimum in the preceding period and enhance the convergence speed in the later period by reducing the population size progressively. The latter strategy can explore wide search ranges and aggregate the individuals by two mutation operators EHMDE/current-to-rand/1 and EHMDE/pbest/1 based on a variation probability. Simulation results of 23 benchmark functions and five ED cases verify the superiority of EHMDE over other peer methods. Furthermore, they also demonstrate that these two improved strategies work well together to strengthen EHMDE.