Energy Management Under Uncertainty for Hybrid Microgrids: From Data to Decision‐Making

ABSTRACT The increasing adoption of distributed energy resources has greatly amplified interest in microgrids, whose effective, reliable and resilient operation relies on the performance of their energy management systems (EMS). These systems ensure the economic operation and maintain load‐generation balance. A practical microgrid EMS (M‐EMS) incorporates data monitoring, variable forecasting, resource allocation and online supervision to optimise the system while interacting with electricity markets. However, in the inherently uncertain environment of both stand‐alone and grid‐connected microgrids, variations in key variables can significantly impact the decision‐making outcomes of M‐EMS. This review paper explores various sources of uncertainties within microgrids, including forecast errors and uncertainties arising from modelling approximations or monitoring inaccuracies. It also provides insights into handling these uncertainties by thoroughly reviewing the pertinent literature and exploring strategies such as analytical methods and AI‐based approaches for capturing them. The eventual goal of handling the uncertainties is to enhance system reliability and security through robust energy management solutions. Furthermore, practical measures to mitigate uncertainties are discussed. The practical implementation of these concepts is illustrated through a review of commercially available M‐EMS solutions and real‐world projects demonstrating their effectiveness in managing energy resources. This paper aims to help both researchers and industry professionals perceive the uncertainties within M‐EMS and how to handle them to achieve accurate, optimal solutions and avoid unexpected costs. Emerging trends and future research directions are also outlined.