Constrained dynamic economic dispatch by simulated annealing/genetic algorithms

This paper proposes a genetic algorithm based on simulated annealing solutions (GA-SA) to solve ramp rate constrained dynamic economic dispatch (DED) problems for generating units with non-monotonically and monotonically increasing incremental cost (IC) functions. Genetic algorithm (GA) uses a simulated annealing (SA) solution as a base solution in order to reduce the search effort towards the optimal solution. The developed GA-SA algorithm is tested on the generating unit systems in the range of 10 to 40 over the entire dispatch periods. As transmission line losses are included, the solutions are near the optimal solutions of zoom brute force (ZBF) and zoom dynamic programming (ZDP), and are less expensive than those obtained from SA, local search (LS), GA based on merit order loading solutions (GA-MOL) and merit order loading (MOL), thereby leading to substantial fuel cost savings. The proposed GA-SA is effective in solving constrained dynamic economic dispatch in terms of the quality of solution.