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Assessment of the potential use of demand response in DHW systems on isolated microgrids

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  • Neves, Diana
  • Pina, André
  • Silva, Carlos A.

Abstract

This work assesses the potential of demand response on isolated hybrid renewable energy systems, in order to optimize the systems' dispatch by minimizing the operation costs and the peak demand. The developed methodology models the implementation of solar thermal systems to replace non-renewable systems for the domestic hot water supply, and a demand response strategy to manage the electric backup required from the grid, in days of low solar radiation. The implementation of this system is compared between 10 isolated islands with different scales and energy systems in order to identify the potential energy savings introduced by solar thermal systems with demand response capabilities under different conditions.

Suggested Citation

  • Neves, Diana & Pina, André & Silva, Carlos A., 2018. "Assessment of the potential use of demand response in DHW systems on isolated microgrids," Renewable Energy, Elsevier, vol. 115(C), pages 989-998.
    • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:989-998
    DOI: 10.1016/j.renene.2017.09.027
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    References listed on IDEAS

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    1. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
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    7. Neves, Diana & Silva, Carlos A., 2014. "Modeling the impact of integrating solar thermal systems and heat pumps for domestic hot water in electric systems – The case study of Corvo Island," Renewable Energy, Elsevier, vol. 72(C), pages 113-124.
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    12. Neves, Diana & Silva, Carlos A. & Connors, Stephen, 2014. "Design and implementation of hybrid renewable energy systems on micro-communities: A review on case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 935-946.
    13. Neves, Diana & Silva, Carlos A., 2015. "Optimal electricity dispatch on isolated mini-grids using a demand response strategy for thermal storage backup with genetic algorithms," Energy, Elsevier, vol. 82(C), pages 436-445.
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    8. Xu, Qingyang & Sun, Feihu & Cai, Qiran & Liu, Li-Jing & Zhang, Kun & Liang, Qiao-Mei, 2022. "Assessment of the influence of demand-side responses on high-proportion renewable energy system: An evidence of Qinghai, China," Renewable Energy, Elsevier, vol. 190(C), pages 945-958.

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