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Towards data-driven energy communities: A review of open-source datasets, models and tools

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  • Kazmi, Hussain
  • Munné-Collado, Íngrid
  • Mehmood, Fahad
  • Syed, Tahir Abbas
  • Driesen, Johan

Abstract

Energy communities will play a central role in the sustainable energy transition by helping inform and engage end users to become more responsible consumers of energy. However, the true potential of energy communities can only be unlocked at scale. This scalability requires data-driven solutions that model not just the behavior of building occupants but also of energy flexible resources in buildings, distributed generation and grid conditions in general. This understanding can then be utilized to improve the design and operation of energy communities in a variety of real-world settings. However, in practice, collecting and analyzing the data necessary to realize these objectives forms a large part of such projects, and is often seen as a prohibitive stumbling block. Furthermore, without a proper understanding of the local context, these projects are often at risk of failure due to misplaced expectations. However, this process can be considerably accelerated by utilizing open source datasets and models from related projects, which have been carried out in the past. Likewise, a number of open source, general-purpose tools exist that can help practitioners design and operate LECs in a near-optimal manner. These resources are important because they not only help ground expectations, they also provide LECs and other relevant stakeholders, including utilities and distribution system operators, with much-needed visibility on future energy and cash flows. This review provides a detailed overview of these open-source datasets, models and tools, and the many ways they can be utilized in optimally designing and operating real-world energy communities. It also highlights some of the most important limitations in currently available open source resources, and points to future research directions.

Suggested Citation

  • Kazmi, Hussain & Munné-Collado, Íngrid & Mehmood, Fahad & Syed, Tahir Abbas & Driesen, Johan, 2021. "Towards data-driven energy communities: A review of open-source datasets, models and tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:rensus:v:148:y:2021:i:c:s1364032121005773
    DOI: 10.1016/j.rser.2021.111290
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    1. Mehmood, Fahad & Umar, Muhammad & Dominguez, Cristina & Kazmi, Hussain, 2022. "The role of residential distributed energy resources in Pakistan's energy transition," Energy Policy, Elsevier, vol. 167(C).
    2. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    3. Lee, Seonho & Kim, Jiwon & Byun, Jaewon & Joo, Junghee & Lee, Yoonjae & Kim, Taehyun & Hwangbo, Soonho & Han, Jeehoon & Kim, Sung-Kon & Lee, Jechan, 2023. "Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach," Applied Energy, Elsevier, vol. 350(C).
    4. Kazmi, Hussain & Tao, Zhenmin, 2022. "How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead," Applied Energy, Elsevier, vol. 323(C).
    5. Heuninckx, Shary & Boveldt, Geert te & Macharis, Cathy & Coosemans, Thierry, 2022. "Stakeholder objectives for joining an energy community: Flemish case studies," Energy Policy, Elsevier, vol. 162(C).
    6. Simoiu, Mircea Stefan & Fagarasan, Ioana & Ploix, Stéphane & Calofir, Vasile, 2022. "Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach," Renewable Energy, Elsevier, vol. 194(C), pages 1233-1246.
    7. Minuto, Francesco Demetrio & Lanzini, Andrea, 2022. "Energy-sharing mechanisms for energy community members under different asset ownership schemes and user demand profiles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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