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Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage

Author

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  • Andrey M. Bramm

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia)

  • Stanislav A. Eroshenko

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia)

  • Alexandra I. Khalyasmaa

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia)

  • Pavel V. Matrenin

    (Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia)

Abstract

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm.

Suggested Citation

  • Andrey M. Bramm & Stanislav A. Eroshenko & Alexandra I. Khalyasmaa & Pavel V. Matrenin, 2023. "Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage," Mathematics, MDPI, vol. 11(11), pages 1-22, June.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2545-:d:1161774
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    References listed on IDEAS

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    1. Uma S. Bhatt & Benjamin A. Carreras & José Miguel Reynolds Barredo & David E. Newman & Pere Collet & Damiá Gomila, 2022. "The Potential Impact of Climate Change on the Efficiency and Reliability of Solar, Hydro, and Wind Energy Sources," Land, MDPI, vol. 11(8), pages 1-18, August.
    2. Ridha, Hussein Mohammed & Hizam, Hashim & Mirjalili, Seyedali & Othman, Mohammad Lutfi & Ya'acob, Mohammad Effendy & Ahmadipour, Masoud, 2023. "Innovative hybridization of the two-archive and PROMETHEE-II triple-objective and multi-criterion decision making for optimum configuration of the hybrid renewable energy system," Applied Energy, Elsevier, vol. 341(C).
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