A systematic review of machine learning techniques related to local energy communities. (December 2022)

• Record Type: Journal Article
• Authors: Hernandez-Matheus, Alejandro; Löschenbrand, Markus; Berg, Kjersti; Fuchs, Ida; Aragüés-Peñalba, Mònica; Bullich-Massagué, Eduard; Sumper, Andreas
• Is Part Of: Renewable & sustainable energy reviews. Volume 170(2022)
• Publication Date: 2022-12
• Subjects: Local energy communities -- Machine learning -- Energy decentralisation -- Energy forecasting -- Energy management systems -- Energy transactions; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112651
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications. Highlights: A conceptualisation of local energy communities from a European perspective. A systematic review of machine learning applications for local energy communities. Analysis of machine learning algorithms for local energy communities' operation.