using improved ddao algorithm to solve economic emission load dispatch problem in the presence of wind farms

In power systems planning, economic load dispatch considering the uncertainty of renewable energy sources is one of the most important challenges that researchers have been concerned about. complex operational constraints, non-convex cost functions of power generation, and some uncertainties make it difficult to solve this problem through conventional optimization techniques. in this article, an improved dynamic differential annealed optimization (iddao) meta-heuristic algorithm, which is an improved version of the dynamic differential annealed optimization (ddao) algorithm has been introduced. this algorithm has been used to solve the economic emission load dispatch (eeld) problem in power systems that include wind farms, and the performance of the proposed technique was evaluated in the ieee 40-unit and 6-unit standard test systems. the results obtained from numerical simulations demonstrate the profound accuracy and convergence speed of the proposed iddao algorithm compared to conventional optimization algorithms including, pso, gsa, and ddao, while independent runs indicate the robustness and stability of the proposed algorithm.