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Algorithm based on particle swarm applied to electrical load scheduling in an industrial setting

Author

Listed:
  • Lopes, Rafael F.
  • Costa, Fabiano F.
  • Oliveira, Aurenice
  • de C. Lima, Antonio Cezar

Abstract

In this work we propose the development of a novel particle swarm-based heuristic to solve a discrete mathematical problem. Such a problem is present in allocating electrical loads throughout the day in an industrial setting. Data on the total installed load and energy demand throughout the day at 15-min intervals were collected in five industrial facilities. The loads were randomly distributed and the developed algorithm was applied to balance and optimize the energy demand throughout the day. The performance of the proposed algorithm was compared to a standard binary Particle Swarm Optimization and a mathematical model, which was also implemented to solve the problem. Our results demonstrate that the proposed algorithm is more efficient for all the considered scenarios, regardless of the amount of loads and constraints applied.

Suggested Citation

  • Lopes, Rafael F. & Costa, Fabiano F. & Oliveira, Aurenice & de C. Lima, Antonio Cezar, 2018. "Algorithm based on particle swarm applied to electrical load scheduling in an industrial setting," Energy, Elsevier, vol. 147(C), pages 1007-1015.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:1007-1015
    DOI: 10.1016/j.energy.2018.01.090
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    References listed on IDEAS

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    1. Herter, Karen & McAuliffe, Patrick & Rosenfeld, Arthur, 2007. "An exploratory analysis of California residential customer response to critical peak pricing of electricity," Energy, Elsevier, vol. 32(1), pages 25-34.
    2. Sezgen, Osman & Goldman, C.A. & Krishnarao, P., 2007. "Option value of electricity demand response," Energy, Elsevier, vol. 32(2), pages 108-119.
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    Cited by:

    1. Rasool Bukhsh & Nadeem Javaid & Zahoor Ali Khan & Farruh Ishmanov & Muhammad Khalil Afzal & Zahid Wadud, 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid," Energies, MDPI, vol. 11(12), pages 1-21, November.

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