Physics-Aware Machine Learning for Integrated Energy Systems Management

by Mohammadreza Daneshvar (Editor), Behnam Mohammadi-Ivatloo (Editor), Kazem Zare (Editor), Jamshid Aghaei (Editor)

Overview

Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management. - Outlines the challenges, opportunities, and applications for utilizing physics-aware machine learning to support renewable energy integration to the modern grid - Covers a wide variety of techniques, from fundamental principles to security concerns - Represents the latest offering in the cutting-edge series, Advances in Intelligent Energy Systems, which introduces these essential multidisciplinary skills to modern energy engineers

Product Details

ISBN-13:	9780443329852
Publisher:	Elsevier Science
Publication date:	08/21/2025
Series:	Advances in Intelligent Energy Systems
Sold by:	Barnes & Noble
Format:	eBook
Pages:	300
File size:	25 MB
Note:	This product may take a few minutes to download.

About the Author

Mohammadreza Daneshvar, PhD, is an Assistant Professor, founder and head of the Laboratory of Multi-Carrier Energy Networks Modernization at the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Prior to that, he was a postdoctoral research fellow in the field of modern multi-energy networks at the Smart Energy Systems Lab of the University of Tabriz for two years. He obtained his MSc and PhD degrees in Electrical Power Engineering from the University of Tabriz, all with honors. He has (co)authored more than 50 technical journal and conference articles, 10 books, 28 book chapters, and 10 national and international research projects in the field. Dr. Daneshvar is a member of the Editorial Board of the Energy and Built Environment Journal and the Early Career Editorial Board of the Sustainable Cities and Society Journal. He also served as the guest editor for the Sustainable Cities and Society, and Sustainable Energy Technologies and Assessments journals. Moreover, he serves as an active reviewer with more than 120 top journals, and was ranked among the top 1% of reviewers in Engineering and Cross-Field based on Publons global reviewer database. His research interests include Smart Grids, Transactive Energy, Energy Management, Renewable Energy Sources, Integrated Multi-Energy Systems, Grid Modernization, Electrical Energy Storage Systems, Sustainable Cities and Society, Microgrids, Energy Hubs, Machine Learning and Deep Learning, Digital Twin, and Optimization Techniques and AI.
Dr. Behnam Mohammadi-Ivatloo, PhD, is a Professor of sector coupling in energy systems at LUT University, Lappeenranta, Finland. He has a mix of high-level experience in research, teaching, administration and voluntary jobs at the national and international levels. He was PI or CO-PI in more than 20 externally funded research projects including grants from EU Horiozn and Business Finland. He is a Senior Member of IEEE since 2017 and a Member of the Governing Board of Iran Energy Association since 2013, where he was elected as President in 2019. He is Editor of IEEE Transactions on Power Systems and IEEE Transactions of Transportation Electrifications. His main areas of interest are integrated energy systems, sector coupling, renewable energies, energy storage systems, microgrids, and smart grids.
Dr. Kazem Zare, PhD, SMIEEE received the B.Sc. and M.Sc. degrees in electrical engineering from University of Tabriz, Tabriz, Iran, in 2000 and 2003, respectively, and Ph.D. degree from Tarbiat Modares University, Tehran, Iran, in 2009. Currently, he is a Professor of the Faculty of Electrical and Computer Engineering, University of Tabriz. His research areas include distribution networks operation and planning, power system economics, microgrid and energy management.
Jamshid Aghaei is currently a Full Professor with the School of Engineering and Technology at Central Queensland University, Australia. His research interests include smart grids, renewable energy systems, electricity markets, and power system operation, optimization, and planning. He was a Guest Editor of the Special Section on “Industrial and Commercial Demand Response” of the IEEE Transactions on Industrial Informatics, in November 2018, and the Special Issue on “Demand Side Management and Market Design for Renewable Energy Support and Integration” of the IET Renewable Power Generation, in April 2019. He is an Associate Editor of the IEEE Transactions on Smart Grid, IEEE Systems Journal, IEEE Transactions on Cloud Computing, IEEE Open Access Journal of Power and Energy, and IET Renewable Power Generation, and a Subject Editor of IET Generation Transmission and Distribution.

Table of Contents

1. Introduction2. The Need for Integrated Energy Systems Management3. Attributes of Integrated Energy Systems in Modern Energy Grids4. Physical-economic Models for Integrated Energy Systems Management5. Decision-making Tools for the Optimal Operation and Planning of Integrated Energy Systems6. Energy Storage Systems for Integrated Energy Systems Management7. Applicability of Machine Learning Techniques in Managing Integrated Energy Systems8. Physics-aware Machine Learning for Integrated Energy Systems Management9. Physics-aware Machine Learning for Improving the Sustainability of Integrated Energy Systems10. Physics-aware Machine Learning for Cyber-security Assessment of Integrated Energy Systems Management11. Physics-aware Reinforcement Learning for Integrated Energy Systems Management12. Physics-aware Feature Learning for Integrated Energy Systems Management13. Physics-aware Neural Networks for Integrated Energy Systems Management14. Physics-aware Machine Learning for Integrated Energy Interaction Management

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From the Publisher

Offers holistic coverage of this multidisciplinary, innovative approach, enabling efficient and sustainable management of integrated energy systems for the modern industry