A new multi‐objective hybrid optimization algorithm for wind‐thermal dynamic economic emission power dispatch - Xia, Aiming; Wu, Xuedong; Bai, Yingjie (International transactions on electrical energy systems, 2021)
Summary This article presents a new optimization method to solve dynamic economic emission dispatch (DEED) problem incorporating wind power by using a hybrid nature inspired multi‐objective algorithm based on equilibrium optimizer (EO) and differential evolution (DE). In the proposed algorithm, the EO with a competitive mechanism and an additional exploration strategy is devised to explore the whole search space, while the DE with a ranking mutation operator and an opposition‐based learning strategy (OBL) is suggested to evolve the individuals of the external archive. The Kent chaotic map is adopted to generate a uniformly distributed initial population. The approach based on non‐dominated sort and improved crowding distance is utilized to screen equilibrium particles' leaders and to update the external archive. These strategies attempt to obtain a Pareto optimal front with excellent diversity and good convergence. Moreover, a real‐time constraints adjustment method and a penalty function method are combined to deal with complex constraints. The simulation results on the test system containing 10 thermal power units and one wind farm indicate that the proposed approach has much better performance than other methods for comparison. This study devises a novel hybrid multi‐objective algorithm with constraints handing based on equilibrium optimizer (EO) and differential evolution (DE) to cope with dynamic economic emission dispatch (DEED) problem incorporating wind power. The simulation results illustrate the excellent performance of this algorithm than other established algorithms.