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

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

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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.
Original languageEnglish
Article number111290
JournalRenewable and Sustainable Energy Reviews
Volume148
Early online date10 Jun 2021
DOIs
Publication statusPublished - 1 Sep 2021

Keywords

  • Data-driven analysis
  • Energy communities
  • Forecasting
  • Open-source
  • Optimal control

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