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Full Description
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.
Contents
1. Introduction to Microgrids, Concepts, Definition, and Classifications. 2. Introduction to Optimization Techniques for Microgrid. 3. Operation Management of Microgrid Clusters. 4. Conventional Control Methods for Microgrids. 5. Distributed Control Approaches for Microgrids. 6. Energy Management Systems for Microgrids. 7. Modeling AC/DC Microgrid. 8. Energy Storage Systems in Microgrids Operation. 9. Intelligent Control of Energy Storage Systems in Microgrids. 10. Intelligent Control for Energy Management. 11. Intelligent Control for Battery Charging System. 12. Microgrid Stability Definition, and Analysis. 13. Heuristic Techniques and Evolutionary Algorithms in Microgrids Optimization Problems. 14. Adaptive Protection Schemes of AC/DC Microgrid. 15. Microgrid Analysis and Case Studies.