The book discusses principles of optimization techniques for microgrid applications specifically for microgrid system stability, smart charging, and storage units. It also highlights the importance of adaptive learning techniques for controlling autonomous microgrids. It further presents optimization-based computing techniques like fuzzy logic, and neural networks to enhance the computational speed. FeaturesDiscusses heuristic techniques and evolutionary algorithms in microgrids optimization problemsCovers operation management, distributed control approaches, and conventional control methods for microgridsPresents intelligent control for energy management and battery charging systemsHighlights a comprehensive treatment of power sharing in DC microgridsExplains control of low-voltage microgrids with master-slave architecture, where distributed energy resources interface with the grid by means of conventional current-driven invertersIt is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, and communications engineering, computer science and engineering, and environmental engineering.