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.

Features

• Discusses heuristic techniques and evolutionary algorithms in microgrids optimization problems
• Covers operation management, distributed control approaches, and conventional control methods for microgrids
• Presents intelligent control for energy management and battery charging systems
• Highlights a comprehensive treatment of power sharing in DC microgrids
• Explains control of low-voltage microgrids with master-slave architecture, where distributed energy resources interface with the grid by means of conventional current-driven inverters

It 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.

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.

Features

• Discusses heuristic techniques and evolutionary algorithms in microgrids optimization problems
• Covers operation management, distributed control approaches, and conventional control methods for microgrids
• Presents intelligent control for energy management and battery charging systems
• Highlights a comprehensive treatment of power sharing in DC microgrids
• Explains control of low-voltage microgrids with master-slave architecture, where distributed energy resources interface with the grid by means of conventional current-driven inverters

It 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.

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.

Features

• Discusses heuristic techniques and evolutionary algorithms in microgrids optimization problems
• Covers operation management, distributed control approaches, and conventional control methods for microgrids
• Presents intelligent control for energy management and battery charging systems
• Highlights a comprehensive treatment of power sharing in DC microgrids
• Explains control of low-voltage microgrids with master-slave architecture, where distributed energy resources interface with the grid by means of conventional current-driven inverters

It 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.