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Multi-area economic dispatch using an improved stochastic fractal search algorithm

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  • Lin, Jian
  • Wang, Zhou-Jing
Abstract
Multi-area economic dispatch (MAED) characterized by high non-convexity and non-linearity is an important issue in power system operation. This paper presents an improved stochastic fractal search (ISFS) to solve the MAED problem considering the area load demands, the tie-line limits and various operating constraints. To balance exploration and exploitation, the ISFS introduces an opposition-based learning method for population initialization as well as for generation jumping. By combining with the differential evolution strategy, a hybrid diffusion process is developed and used as the local search technique to enhance the exploitation ability. Furthermore, a novel repair-based penalty approach is presented and incorporated into the ISFS to find feasible solutions more efficiently. The effectiveness and robustness of the ISFS is evaluated on several test systems consisting of 16–120 generating units. Computational results demonstrate the superiority of the proposed ISFS scheme over the state-of-the-art algorithms.

Suggested Citation

  • Lin, Jian & Wang, Zhou-Jing, 2019. "Multi-area economic dispatch using an improved stochastic fractal search algorithm," Energy, Elsevier, vol. 166(C), pages 47-58.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:47-58
    DOI: 10.1016/j.energy.2018.10.065
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    References listed on IDEAS

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    Cited by:

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