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.
|Number of pages||14|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics: Systems|
|Publication status||Published - Apr 2021|
Bibliographical noteFunding Information:
Manuscript received July 18, 2019; accepted October 29, 2019. Date of publication November 27, 2019; date of current version March 17, 2021. This work was supported by the National Natural Science Foundation of China under Grant 61502544 and Grant 61332002. This article was recommended by Associate Editor H. Tianfield. (Corresponding author: Wei-Jie Yu.) J.-Y. Ji is with the School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China. W.-J. Yu is with the School of Information Management, Sun Yat-sen University, Guangzhou 510006, China (e-mail: email@example.com). J. Zhong is with the School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China. J. Zhang is with the Division of Electrical Engineering, Hanyang University, Ansan 15588, South Korea. This article has supplementary downloadable material available at https://ieeexplore.ieee.org, provided by the author. Color versions of one or more of the figures in this article are available online at https://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMC.2019.2953336
© 2013 IEEE.
- Differential evolution (DE)
- dynamic constraint-handling technique
- economic dispatch (ED) problem
- multiobjective optimization