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What metaheuristic solves the economic dispatch faster? A comparative case study

H. Abdi, H. Fattahi, S. Lumbreras

The economic dispatch (ED) is one of the most important short-term problems in power systems, and solving it quickly is essential. However, classical optimization tools are often too computationally demanding to be considered satisfactory. This has motivated the application of metaheuristic methods, which offer a good compromise in terms of solution quality and computation time. However, these methods have been applied in an isolated way and on different problem definitions and case studies, so that there were no clear insights on how they compared to each other. This paper fills this gap by performing an objective comparison of six metaheuristics solving the ED in several case studies under different conditions. Although mixed-integer programming performs best for small case studies, our results confirm that metaheuristics are able to efficiently solve the ED problem. Genetic algorithms emerge as the best performers in terms of solution quality and computation time, followed by PSO and TLBO.


Keywords: Economic dispatch; Heuristic algorithms; Evolutionary computation; Genetic algorithms; Particle swarm optimization


Electrical Engineering. Volumen: 100 Numero: 4 Páginas: 2825-2837

Journal Impact Factor: JCR impact factor 1.269 (2017)

DOI reference: DOI icon 10.1007/s00202-018-0750-4    

Publicado en papel: Diciembre 2018. Publicado on-line: Octubre 2018.


    Líneas de investigación:
  • Modelos de mercados eléctricos con alta penetración de generación renovable

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