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An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects

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  • Meng, Anbo
  • Li, Jinbei
  • Yin, Hao

Abstract

As one of important optimization problems in power system, economic dispatch (ED) with multiple fuel options is characterized by high non-convexity, non-linearity and discontinuity. The combined action of multiple fuel options and valve-point effects increases the degree of difficulty to solve the ED problem. In this paper, a recently developed heuristic algorithm called crisscross optimization algorithm (CSO) is attempted to address the large-scale and non-convex ED problem with both multiple fuel options and valve-point effects taken into account. The proposed CSO method solves the ED problem through horizontal crossover and vertical crossover. The former searches for the new solutions within a half population of hyper-cubes by adopting a cross-border search approach while the latter provides a unique mechanism to prevent from the premature convergence problems based on the concept of dimensional local minimum. Both operators alternatively generate moderation solutions which are subsequently updated by an elite selection strategy. The proposed method is validated on six test systems consisting of 10–640 generating units and compared with other state-of-the-art methods in the literature. The results show that CSO yields higher quality solutions especially for solving large-scale ED problems with multiple fuel options.

Suggested Citation

  • Meng, Anbo & Li, Jinbei & Yin, Hao, 2016. "An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects," Energy, Elsevier, vol. 113(C), pages 1147-1161.
    • Handle: RePEc:eee:energy:v:113:y:2016:i:c:p:1147-1161
    DOI: 10.1016/j.energy.2016.07.138
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    Cited by:

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    17. 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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