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

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 behaviour 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 analysing 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. Highlights:1. The importance of open-source datasets and tools for local energy communities 2. Common use cases for open-source datasets, models and tools for energy communities 3. A thorough review of electricity demand and meteorological datasets and models 4. Most important shortcomings with currently available datasets, models and tools

How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead

“…Consequently, these data or information flows transcend the responsibility or jurisdiction of any individual transmission system operator (TSO). While several papers point out a general lack of open data in the energy sector [35], in this case the transparency platform from ENTSO-E fills this gap and has been widely utilized in research [36]. Several papers have also appeared discussing its extensions [37] and limitations [38].…”

Energy democracy: A digital future?

“…Ownership of software is perhaps clearer from a legal perspective as it is more consistently protected by intellectual property regimes. However, there is also significant momentum in academic, non-profit and developer communities to encourage publishing of 'open source' code that can be both used and scrutinized by others with few (if any) limiting conditions [121]. Furthermore, in some countries such as the UK, governance bodies are also moving towards encouraging open source publishing as well as the creation of open standards governing software development in certain domains [122].…”

Free and Open-Source Automated Open Access Preprint Harvesting

“…There are well-known benefits not only for science but also for researchers for using open research practices: increases in citations, media attention, potential collaborators, job opportunities, and funding opportunities (McKiernan et al, 2016). To optimize this convergence, aperta-accessum could be expanded to other areas of open science (Spellman et al, 2017), including the following: data sets (Chen et al, 2018;Kazmi et al, 2021); FOSS (Von Krogh & Von Hippel, 2006;Von Krogh & Spaeth, 2007); free and open source hardware (FOSH) (Pearce, 2013;Pearce, 2015;Pearce, 2016;Maia Chagas, 2018); and new Elsevier companion journals such as Data in Brief, MethodsX, SoftwareX, and HardwareX.…”