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

João Faria, Carlos Marques, José Pombo, Sílvio Mariano () and Maria do Rosário Calado
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João Faria: IT—Instituto de Telecomunicações, Faculty of Engineering, 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
Sílvio Mariano: IT—Instituto de Telecomunicações, Faculty of Engineering, 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

Energies, 2023, vol. 16, issue 21, 1-33

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.

Keywords: renewable energy community (REC); energy management strategies; multi-objective optimization algorithm; multi-swarm MOPSO; energy storage systems; energy storage sharing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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