Growing shares of intermittent clean generation as well as variable loads lead to challenges for distribution systems at all levels. Energy management systems (EMS) are systems of computer-aided tools used by operators of electric utility grids to monitor, control, and optimize the performance of the generation, distribution or transmission system. EMS help to ensure grid stability and power quality, and can also be used in smaller scale systems such as microgrids.
Energy Management Systems for Microgrids with Wind, PV and Battery Storage gives a broad overview of EMS technologies for researchers, designers, operators at electric utilities involved with managing power systems, as well as advanced energy engineering students working on power systems.
Chapters cover AC network performance with flexible alternating current transmission system (FACTS) devices, metaheuristic optimization and hidden neuron count effect on microgrid management. Ensuing chapters will focus on microgrids with storage, with control of microgrids with renewables and storage, while several chapters look at wind energy in smart grids, battery charge in wind turbines, field-programmable gate array for wind turbines, fuzzy logic for wind energy, and classical Boolean methods for hybrid systems with wind, PV and batteries. Later chapters convey model predictive control and particle swarm optimization, and integration of and energy management for EV with support vector regression.
Chapter 1: AI and IoT for cost and emission reduction in microgrid
energy management
Chapter 2: Improvement of AC network performance using shunt and hybrid FACTS
devices
Chapter 3: Metaheuristic optimization of electrical distribution systems with
energy storage and reactive compensators
Chapter 4: Classification and hidden neuron count effect on renewable
microgrid power management
Chapter 5: Energy management and voltage control strategy in microgrid using
PV and battery
Chapter 6: Small scale renewable energies and storage for microgrids
Chapter 7: Advancing sustainable energy: integrating small-scale renewables
and storage in microgrids
Chapter 8: Smart grids with wind energy
Chapter 9: Battery charge controllers in wind turbine systems
Chapter 10: Field-programmable gate array implementation: power management
for wind turbines with battery
Chapter 11: Fuzzy logic for grid-connected doubly fed induction generator for
wind energy
Chapter 12: Energy management with classical Boolean method for
grid-connected hybrid systems with wind, PV and battery
Chapter 13: Control of photovoltaic-wind energy systems using MPC and PSO
Chapter 14: Integration of electrical vehicle into smart grid, opportunities
and challenges
Chapter 15: Energy management for EV: state-of-charge estimation in Li-ion
batteries with support vector regression hybrid approach
Chapter 16: Conclusions and outlook
Badre Bossoufi (https://orcid.org/0009-0009-6506-8464) (Eng., Ph.D., IEEE Senior Member) received his Ph.D. in electrical engineering from the Faculty of Sciences at Sidi Mohamed Ben Abdellah University in Fez, and a joint Ph.D. from the Faculty of Electronics and Computer at the University of Piteti, Romania, and the Montefiore Institute of Electrical Engineering in Liège, Belgium, in 2012. He was a professor of electrical engineering at the Faculty of Sciences Dhar El Mahraz at Sidi Mohamed Ben Abdellah University. His research interests include static converters, electrical motor drives, power electronics, smart grids, renewable energy, and artificial intelligence. He has published numerous papers in journals and conferences over the past few years, most of which relate to wind power control and microgrid systems. He has edited several books and served as a guest editor for various special issues and topical collections. He is a reviewer and is on the editorial boards of several journals. He has been associated with more than 20 international conferences as a program committee member, advisory board member, or review board member.
