A Constraints Handling Method for Evolutionary Optimization Algorithm in Solving Hybrid Dynamic Economic Emission Dispatch

With the rapid development of renewable energy, hybrid dynamic economic emission dispatch (HDEED) which considering renewable energy has gradually become a heated topic. HDEED is a non-convex, strongly constrained, high-dimensional multi-objective optimization problem, which usually solved by evolutionary optimization algorithm. In order to meet the constraints in HDEED, this paper proposes a new constraints handling method. The proposed method distributes the imbalance power among power generators in a weighted or prioritized way under the premise of preferential consumption of renewable energy and satisfying inequality constraints. The method not only meets all constraints in HDEED problem, but also improves convergence of the algorithm and the diversity of the Pareto solution set. A case involving thermal power units, wind farms and photovoltaic plant is used to test the performance of the proposed method which combined with a classical multi-objective evolutionary optimization algorithm NSGA-II. The result shows that the proposed method is effective and superior to the compared methods.