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Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

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  • João Faria

    (IT—Instituto de Telecomunicações, Faculty of Engineering, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Carlos Marques

    (Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • José Pombo

    (IT—Instituto de Telecomunicações, Faculty of Engineering, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Sílvio Mariano

    (IT—Instituto de Telecomunicações, Faculty of Engineering, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Maria do Rosário Calado

    (IT—Instituto de Telecomunicações, Faculty of Engineering, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

Abstract

Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

Suggested Citation

  • João Faria & Carlos Marques & José Pombo & Sílvio Mariano & Maria do Rosário Calado, 2023. "Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach," Energies, MDPI, vol. 16(21), pages 1-33, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7227-:d:1266222
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    References listed on IDEAS

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    1. Weckesser, Tilman & Dominković, Dominik Franjo & Blomgren, Emma M.V. & Schledorn, Amos & Madsen, Henrik, 2021. "Renewable Energy Communities: Optimal sizing and distribution grid impact of photo-voltaics and battery storage," Applied Energy, Elsevier, vol. 301(C).
    2. Huo, Yuchong & Bouffard, François & Joós, Géza, 2021. "Decision tree-based optimization for flexibility management for sustainable energy microgrids," Applied Energy, Elsevier, vol. 290(C).
    3. Sadeghi, Delnia & Hesami Naghshbandy, Ali & Bahramara, Salah, 2020. "Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization," Energy, Elsevier, vol. 209(C).
    4. Lin, Jason & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Comparative analysis of auction mechanisms and bidding strategies for P2P solar transactive energy markets," Applied Energy, Elsevier, vol. 255(C).
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    Cited by:

    1. Gianluca Carraro & Enrico Dal Cin & Sergio Rech, 2024. "Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities," Energies, MDPI, vol. 17(24), pages 1-20, December.
    2. Pagnini, Luisa & Bracco, Stefano & Delfino, Federico & de-Simón-Martín, Miguel, 2024. "Levelized cost of electricity in renewable energy communities: Uncertainty propagation analysis," Applied Energy, Elsevier, vol. 366(C).

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