Paper Info
Title
Density-Enhanced Multiobjective Evolutionary Approach for Power Economic Dispatch Problems
Abstract
Economic dispatching of generating units in a power system can significantly reduce the energy cost of the system. However, the economic dispatch (ED) problem is highly constrained, and often has disconnected feasible regions because of various physical features. Enhancing population diversity is critical for the evolutionary approach to fully explore and exploit the feasible regions. In this article, we propose a density-enhanced multiobjective evolutionary approach to solve ED problem. An ED problem is first transformed into a tri-objective optimization problem, and then multiobjective optimization techniques are employed to fully optimize the constraints and cost function simultaneously. The first two objectives are derived from the original ED problem, while the third one is a novel density objective constructed by niching methods to enhance population diversity. These three objectives are optimized simultaneously by a dynamic dominance relation, which can make a good balance among feasibility, diversity, and convergence. To evaluate the performance of this proposed approach, 22 benchmark problems and seven real-world ED problems with different features are tested in this article. The experimental results show that our approach performs better than or at least competitive to the state-of-the-art algorithms, especially on large-scale ED problems.
Year
DOI
Venue
2021
10.1109/TSMC.2019.2953336
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Differential evolution (DE),dynamic constraint-handling technique,economic dispatch (ED) problem,multiobjective optimization
Journal
51
Issue
ISSN
Citations 
4
2168-2216
0
PageRank 
References 
Authors
0.34
25
4
Name
Order
Citations
PageRank
Jing-Yu Ji1102.17
Wei-jie Yu21088.47
Jing-hui Zhong338033.00
Jun Zhang42491127.27