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Microgrids (MGs) are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, as well as a more resilient and economical on/off-grid control, operation, and energy management. However, MGs, as newcomers to the utility grid, are also facing challenges due to economic deregulation of energy systems, restructuring of generation, and market-based operation. This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques. First, MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management. Second, energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management. Mathematical programming, adaptive dynamic programming, and deep reinforcement learning-based solution methods are investigated accordingly, together with their implementation schemes. Finally, problems for future energy management systems with dynamics-captured critical component models, stability constraints, resilience awareness, market operation, and emerging computational techniques are discussed.
Liu et al. (Sun,) studied this question.