This paper reviews the system components, modeling, and control of microgrids for future smart buildings in current literature. Microgrids are increasingly widely studied due to their reliability in the event of grid failure or emergency, their incorporation of renewable energy sources, and the potential they represent for overall cost reduction for the consumer. Greater accuracy in microgrid modeling enables the design of more advanced control methods, resulting in better objective optimization. This paper begins with an overview of microgrids and their components, their importance to both utility providers and building owners, and typical problems that they may be used to solve, as well as modeling challenges that microgrid researchers may face. An overview of microgrid control and optimization is given in terms of objectives, constraints, and optimization methods. Microgrid modeling is a complex task due to the number, variety, and complexity of microgrid components, which can include building loads, distributed energy resources, and energy storage systems. Various component modeling methods including physics-based and data-driven models are reviewed, to include battery degradation models. Furthermore, this paper provides a review of various data-driven forecasting methods for the microgrid controls. Different types of control methods including rule-based and model predictive control are reviewed, including latest occupancy-based model predictive control for buildings. Lastly, a discussion of current challenges that may be faced by researchers is presented, as well as future directions.