A new multi‐objective hybrid optimization algorithm for wind‐thermal dynamic economic emission power dispatch

Abstract

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.

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

“…Therefore, we define the objective function as the error between the desired position and the current position. As shown in equation (23).…”

“…Comparing with the classic algorithm and other hybrid models, the HGPSODE algorithm has better performance. Xia et al 23 and Xia and Wu 24 proposed two hybrid multi-objective optimization algorithms, and applied them to solve the problem of dynamic economic emission dispatching in the environment where wind power. One is a hybrid algorithm by equilibrium optimizer (EO) and DE algorithms, and the other is a hybrid algorithm by marine predators (MPA) and DE algorithms.…”

Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems

“…Ref. [19] suggested a novel multi-objective hybrid optimization-algorithm-based equilibrium optimizer (EO) and differential evolution (DE) to solve the DEED. The proposed algorithm is validated and verified on the test system containing ten thermal power generators and one wind farm.…”

Wind‐driven water wave optimized economic load dispatch for the integration of renewable energy sources in micro‐grid system

“…This approach diminishes the generation cost and emission consecutively. The multi‐objective was developed by Xia et al 20 to solve dynamic ED issues by incorporating wind and PV. The RES had not fulfilled the energy demand due to their stochastic nature of solar and wind resources.…”