Exploring the role of Energy Communities: A Comprehensive Review

Abstract

The Energy Communities (EC) framework facilitates the active engagement of energy entities. It brings about a fundamental shift in the energy sector by effectively managing Distributed Energy Resources (DERs) and advancing a decentralized energy system. The implementation of this technology allows the electrification of rural or mountainous regions by addressing the obstacles associated with power grid maintenance through substantial restructuring of the underlying energy distribution framework. The present review aimed to investigate and examine the significance of EC structures and to start an inclusive foundation for the broader implementation of EC for energy decentralization to figure out the research gaps and ensure that Machine Learning (ML) based solutions are essential tools to study and further discussed the several ML-based algorithms based on their objectives. Moreover, a comprehensive literature review is conducted to compare the possible strategies and tools that could be implemented in EC. Furthermore, different solution tools are organized based on their advantages, such as Demand Response (DR), forecasting, and load management goals. Hence, it can be inferred that Reinforcement Learning (RL) methodologies exhibit considerable potential in the control field. In contrast, supervised and unsupervised approaches are essential in predicting tasks. Based on the existing knowledge, the present study can conclude that ML-based solution methods are of significant importance for developing an effective energy decentralization platform.