Reviewing the frontier: modeling and energy management strategies for sustainable 100% renewable microgrids

The surge in global interest in sustainable energy solutions has thrust 100% renewable energy microgrids into the spotlight. This paper thoroughly explores the technical complexities surrounding the adoption of these microgrids, providing an in-depth examination of both the opportunities and challenges embedded in this paradigm shift. The review examines pivotal aspects, including intricate modelling methodologies for renewable energy sources, real-time energy management systems, and sophisticated strategies for navigating short-term uncertainties. Innovative approaches to real-time energy management are dissected for their potential to tune operational efficiency finely. Furthermore, the study investigates methodological frameworks to address short-term uncertainty, leveraging cutting-edge techniques such as machine learning, robust optimization, and information gap decision theory. Despite the pivotal role short-term uncertainty plays, it frequently occupies a subordinate position in research, eclipsed by the presumption of minimal economic impact. This study challenges this prevalent notion, underscoring the indispensable need for exhaustive research on uncertainty. Such comprehensive exploration is essential to ensure the practicality and sustainability of 100% renewable energy grids. The paper concludes by emphasizing the importance of addressing short-term uncertainty and providing nuanced insights that can facilitate the effective implementation and ongoing development of these grids within the dynamic landscape of electrical energy systems. Delve into advanced modelling techniques for renewable sources in 100% renewable microgrids, unravelling technical intricacies. Analyze cutting-edge real-time energy management strategies, aiming for precision in operational efficiency. Challenge prevailing notions on short-term uncertainty, advocating for robust methodologies leveraging machine learning and decision theory in 100% renewable energy grids.