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Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects

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  • Chen, Xu

Abstract

This paper presents a novel dual-population adaptive differential evolution (DPADE) algorithm to solve the complex large-scale and non-convex ED problems considering both multi-fuel options and valve-point effects. To increase its competitiveness for large-scale multi-fuel ED problems, DPADE introduces two improvements to differential evolution. First, a dual-population framework is employed to improve the search efficiency, in which the elite and common populations are updated based on different strategies. Second, an adaptive technology is adopted to adjust two important control parameters and avoid the inappropriate parameters. Furthermore, to ensure the feasibility of the dispatch solutions, a repair method is used to deal with the optimization constraints. The proposed DPADE is applied to solve six multi-fuel non-convex ED problems, including three extremely large-scale cases with 320, 640 and 1280 generating units. The results are compared with those of seven well-established optimization methods. It is observed that DPADE has advantages in solution accuracy and convergence speed when solving the large-scale multi-fuel ED problems.

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  • Chen, Xu, 2020. "Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220309816
    DOI: 10.1016/j.energy.2020.117874
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    5. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).

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