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A combined optimisation and decision-making approach for battery-supported HMGS

Author

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  • Carolina Marcelino
  • Manuel Baumann
  • Leonel Carvalho
  • Nelson Chibeles-Martins
  • Marcel Weil
  • Paulo Almeida
  • Elizabeth Wanner

Abstract

Hybrid micro-grid systems (HMGS) are gaining increasing attention worldwide. The balance between electricity load and generation based on fluctuating renewable energy sources is a main challenge in the operation and design of HMGS. Battery energy storage systems are considered essential components for integrating high shares of renewable energy into a HMGS. Currently, there are very few studies in the field of mathematical optimisation and multi-criteria decision analysis that focus on the evaluation of different battery technologies and their impact on the HMGS design. The model proposed in this paper aims at optimising three different criteria: minimising electricity costs, reducing the loss of load probability, and maximising the use of locally available renewable energy. The model is applied in a case study in southern Germany. The optimisation is carried out using the C-DEEPSO algorithm. Its results are used as input for an AHP-TOPSIS model to identify the most suitable alternative out of five different battery technologies using expert weights. Lithium batteries are considered the best solution with regard to the given group preferences and the optimisation results.

Suggested Citation

  • Carolina Marcelino & Manuel Baumann & Leonel Carvalho & Nelson Chibeles-Martins & Marcel Weil & Paulo Almeida & Elizabeth Wanner, 2020. "A combined optimisation and decision-making approach for battery-supported HMGS," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 762-774, May.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:5:p:762-774
    DOI: 10.1080/01605682.2019.1582590
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    Cited by:

    1. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    2. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    3. Carolina Gil Marcelino & Carlos Camacho-Gómez & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm," Energies, MDPI, vol. 14(9), pages 1-24, April.
    4. Kamali Saraji, Mahyar & Aliasgari, Elahe & Streimikiene, Dalia, 2023. "Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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