This book covers the optimization of the energy flow management of a grid-tied photovoltaic-wind-battery energy storage system and the development of demand response algorithms for a single customer at the lowest level and extended it to a large community of participants at the highest level. Hence, this book consists of two parts. The first part is devoted to the design of energy management strategies for a residential PV-WT system, while considering time of use and feed in tariff schemes. Further, the developed management algorithms mitigate the greenhouse gas emissions. Additionally, a closed-loop control based-model predictive control scheme, to forecast the system behavior and deal with renewable energy resources and load disturbances, is elaborated. The second part is dedicated to developing eective demand side management techniques and cooperative energy scheduling in the framework of collective smart-buildings. Cooperative energy scheduling programs are designed while considering three objectives: minimizing peak load, minimizing electricity cost, and avoiding the occurrence of rebound peaks. Optimal consumption decisions are examined in residential, commercial, and industrial areas. Besides, the impact of electric vehicle charging methods is investigated for dierent EV penetration levels. Also, a game theory approach for energy consumption scheduling of a community of smart microgrids is built. In summary, this book successfully establishes the important synergy between microgrid energy management and demand response at the level of a smart home and an aggregated building community. The above aspects are illustrated in this book in 12 chapters. The scope is broad. This book chapters are very effective in theoretical analysis and design of optimization algorithms in the realm of smart grids. This book is intended to provide advanced undergraduate, graduate students, engineers, researchers on energy management within smart homes and clustered buildings, with a comprehensive understanding of various methods and designed algorithms. These strategies and algorithms aim to deal with defies ranging from augmented adoption of microgrids to the introduction of demand response approaches, to enhance the grid viability and resilience.

Provides fruitful theoretical developments and effective optimization algorithms in the realm of smart grids Covers the optimization of the energy flow management of a grid-tied photovoltaic-wind-battery energy storage system Focuses on energy management within smart homes and clustered buildings,

Autorentext

"Prof. Mohammed OUASSAID received his Diploma of Agrégation degree in Electrical Engineering from Ecole Nationale Supérieure d’Art et Métiers de Rabat (ex ENSET), in 1999, and MScA and Ph.D. degrees in Electrical Engineering from Mohammadia School of Engineers (EMI), Mohamed V University in Rabat, Morocco, in 2002 and 2006, respectively. In 2012, he received the diploma of University’s Habilitation. During 2008-2015, he was a Professor at National School of Applied Sciences (ENSA-Safi) Cadi Ayyad University, Marrakesh, Morocco. In 2015, he joined the Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco, where he is currently a Full Professor. As a professor, he has taught many courses and labs, including Power System Architecture and Analysis, DC and AC Electric Machines, Control of Electric Drives, Renewable Distributed Generation and Storage, Industrial Electronics, Power Quality, Electrical Circuits and Devices, AC Electrical Circuits Analysis, DC Electrical Circuits Analysis, Smart Grid and Integration of Renewable Energies. Prof. Mohammed OUASSAID is the author or coauthor of a book, numerous book chapters and more than 270 papers in refereed journals and international conference proceedings. He is a permanent reviewer of many credible journals. His main research interests include Electrical Drives, Power Systems, Control and Optimization of Renewable Energy, Integration of Wind Power and Photovoltaic Systems, Energy Scheduling and Demand-Side Management, Plug-in Electric Vehicles, Control and Management of Microgrid and Smart Grid. Prof. Mohammed OUASSAID is also involved in a number of research and teaching projects. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the IEEE Power & Energy Society. Prof. Mohammed OUASSAID is a Scientific evaluator expert with the National Center for Scientific and Technical Research –CNRST- (February 2023-present)." "Dr. Raja Naji El Idrissi earned her M.S. degree in Renewable Energies and Storage from the Faculty of Sciences, Mohammed V University in Rabat, Morocco, in 2016. She later received her Ph.D. in Electrical Engineering from the Mohammadia School of Engineers, Mohammed V University in Rabat, in 2023. Currently, Dr. El Idrissi serves as an Associate Researcher at the Engineering for Smart and Sustainable Systems Research Center (E3S), Mohammed V University in Rabat. Her research focuses on smart grids, energy management studies, optimization-based methods for energy management and control in smart grids. She has authored and co-authored numerous publications in these research areas." Dr. Meryme Azaroual was born in Fez, Morocco. She received the M.S. degree in Renewable Energy and Storage from Faculty of Sciences, Mohammed V University in Rabat, Morocco, in 2016. She received her Ph.D. in Electrical Engineering from Mohammadia School of Engineers, Mohammed V University in Rabat in 2022. Dr. Azaroual is currently an Associate Researcher at the Engineering for Smart and Sustainable Systems Research Center (E3S), Mohammed V University in Rabat. Her main research interests include renewable energy, energy efficiency and management, optimization and control of intelligent systems in residential application. She has published multiple papers in these fields."

Inhalt

Microgrid Structure and Control Methods A Review.- Microgrid System Operation.- Energy Management of Residential Microgrid under Step-rate and Time-of-Use Tariffs.- Microgrid Energy Flow Management Considering Cost, Reliability and 𝑪𝑶2 emissions.- Model Predictive Control Based Strategy for Hybrid Microgrid.- A Survey on Home Energy Management.- DSM Strategy Using Optimal Day-Ahead Load Shifting.- Peak Load Minimization Through Electricity Pricing and Cooperative Load Scheduling.- Collective Energy Consumption Scheduling for Aggregated Smart Buildings.- Coordinated Blockchain Technique for Load Scheduling within Smart Buildings.- Efficient Charging Scheduling of Aggregated Electric Vehicles.- Summary and Scope.