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Impact of service sector loads on renewable resource integration

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  • Voulis, Nina
  • Warnier, Martijn
  • Brazier, Frances M.T.

Abstract

Urban areas consist of a mix of households and services, such as offices, shops and schools. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management.

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  • Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2017. "Impact of service sector loads on renewable resource integration," Applied Energy, Elsevier, vol. 205(C), pages 1311-1326.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1311-1326
    DOI: 10.1016/j.apenergy.2017.07.134
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    4. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2018. "Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach," Applied Energy, Elsevier, vol. 230(C), pages 1157-1171.
    5. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
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    7. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    8. Lauma Balode & Kristiāna Dolge & Dagnija Blumberga, 2023. "Sector-Specific Pathways to Sustainability: Unravelling the Most Promising Renewable Energy Options," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    9. Collaço, Flávia Mendes de Almeida & Dias, Luís Pereira & Simoes, Sofia G. & Pukšec, Tomislav & Seixas, Júlia & Bermann, Célio, 2019. "What if São Paulo (Brazil) would like to become a renewable and endogenous energy -based megacity?," Renewable Energy, Elsevier, vol. 138(C), pages 416-433.
    10. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    11. Voulis, Nina & van Etten, Max J.J. & Chappin, Émile J.L. & Warnier, Martijn & Brazier, Frances M.T., 2019. "Rethinking European energy taxation to incentivise consumer demand response participation," Energy Policy, Elsevier, vol. 124(C), pages 156-168.

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