Solving Dynamic Economic Dispatch of Generating Units Using Grey Wolf Optimizer Algorithm

The dynamic economic dispatch problems can be either single-objective or multi-objective depending on the concerns of the operator, and it is used in order to minimize the total generation cost and transmission losses. In this research, the day-ahead dynamic economic dispatch is investigated that involves diesel generators to find a solution with minimum transmission losses, a smaller number of iterations, and a smaller computational time. The aim of this study is to find the optimal schedule for the generation that minimizes the generation cost and transmission losses of generating units for a 24 h period. Grey wolf optimizer algorithm (GWO) is used in the study to deal with the dynamic economic dispatch problem, whose results are compared with results of many other powerful algorithms. The proposed approach is tested on two benchmark systems with 5 and 10 generating units over a 24 h scheduling horizon. The simulation results are achieved by grey wolf optimizer algorithm then compared with different algorithms and techniques in order to prove the high-quality effectiveness of grey wolf optimizer algorithm in handling the dynamic economic dispatch problem especially in minimizing transmission losses including all the supply-demands equality constraints, valve point effects, ramp rate constraints, upper and lower generation limits for each generating unit. The results show that the GWO attains competitive generation cost minimization while effectively reducing transmission losses, with rapid convergence and modest computational requirements. Comparisons with several state-of-the-art optimization methods further demonstrate the robustness and effectiveness of the proposed framework.