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Consensus-based optimal operation of multi-agent renewable energy hubs considering various graph topologies

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  • Ghappani, Seyyed Aliasghar
  • Khanahmadi, Abbas
  • Ghaffarpour, Reza

Abstract

In recent years, energy hubs (EHs) have emerged as active, flexible, and intelligent structures that integrate the generation, storage, and conversion of multiple energy carriers to satisfy diverse energy demands. This study aims to optimize and coordinate multiple EHs using an iterative consensus algorithm based on various graph topologies, thereby enhancing EH operation from both economic and environmental perspectives. The proposed algorithm facilitates distributed decision-making, reduces dependence on upstream electricity and natural gas networks, and optimizes the management of uncertainties, thereby improving system reliability. In this study, the uncertainties associated with renewable energy sources (RESs), such as solar irradiation and wind speed, are considered. Additionally, hydrogen is introduced as a clean, renewable, and modern energy carrier to effectively manage these uncertainties. Key advantages of the proposed method include convergence to a specific value, comprehensive management of all EHs, minimization of computational time, and identification of an optimal connection topology among EHs. The effectiveness of the proposed consensus algorithm is validated through simulation results based on specific criteria, including purchased electricity and natural gas, operational costs, and environmental emissions. The results demonstrate a 72.5 % and 72.7 % reduction in purchased electricity and natural gas, respectively. Moreover, operational costs and environmental emissions decrease by 73.5 % and 74.5 %, respectively, compared to the total values obtained using the centralized method (CM).

Suggested Citation

  • Ghappani, Seyyed Aliasghar & Khanahmadi, Abbas & Ghaffarpour, Reza, 2025. "Consensus-based optimal operation of multi-agent renewable energy hubs considering various graph topologies," Renewable Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:renene:v:251:y:2025:i:c:s0960148125009930
    DOI: 10.1016/j.renene.2025.123331
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    References listed on IDEAS

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