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Optimal Operation of Integrated Energy Systems Under Uncertainties
Distributionally Robust and Stochastic Methods
- 1st Edition - September 6, 2023
- Authors: Bo Yang, Zhaojian Wang, Xinping Guan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 4 1 2 2 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 4 1 2 3 - 2
Optimal Operation of Integrated Energy Systems Under Uncertainties: Distributionally Robust and Stochastic Models discusses new solutions to the rapidly emerging concerns surroundi… Read more
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Request a sales quoteOptimal Operation of Integrated Energy Systems Under Uncertainties: Distributionally Robust and Stochastic Models discusses new solutions to the rapidly emerging concerns surrounding energy usage and environmental deterioration. Integrated energy systems (IESs) are acknowledged to be a promising approach to increasing the efficiency of energy utilization by exploiting complementary (alternative) energy sources and storages. IESs show favorable performance for improving the penetration of renewable energy sources (RESs) and accelerating low-carbon transition. However, as more renewables penetrate the energy system, their highly uncertain characteristics challenge the system, with significant impacts on safety and economic issues.
To this end, this book provides systematic methods to address the aggravating uncertainties in IESs from two aspects: distributionally robust optimization and online operation.
- Presents energy scheduling, considering power, gas, and carbon markets concurrently based on distributionally robust optimization methods
- Helps readers design day-ahead scheduling schemes, considering both decision-dependent uncertainties and decision-independent uncertainties for IES
- Covers online scheduling and energy auctions by stochastic optimization methods
- Includes analytic results given to measure the performance gap between real performance and ideal performance
Postgraduate students, faculties and researchers from research institutes, Engineers with research and practical experiences in the field of Power and Energy
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter 1: Introduction
- Abstract
- 1.1. Integrated energy systems
- 1.2. Challenges on the energy management
- 1.3. Related work
- 1.4. Overview of the book
- References
- Chapter 2: Day-ahead energy management of IES with a distributionally robust approach
- Abstract
- 2.1. Introduction
- 2.2. System model and problem formulation
- 2.3. Scheduling with distributionally robust optimization
- 2.4. Numerical experiments
- 2.5. Conclusion and notes
- References
- Chapter 3: Distributionally robust heat-and-electricity pricing for IES with decision-dependent uncertainties
- Abstract
- 3.1. Introduction
- 3.2. System model and problem formulation
- 3.3. Solution approach
- 3.4. Computational experiments
- 3.5. Conclusion and notes
- References
- Chapter 4: Multi-level coordinated energy management for IES in hybrid markets
- Abstract
- 4.1. Introduction
- 4.2. Energy-management architecture
- 4.3. Day-ahead energy-management mechanism in hybrid markets
- 4.4. Intra-day energy-management mechanism
- 4.5. Solution methodology
- 4.6. Computational experiments
- 4.7. Conclusion and notes
- References
- Chapter 5: Energy management based on multi-agent deep reinforcement learning for IES
- Abstract
- 5.1. Introduction
- 5.2. System model
- 5.3. MADRL algorithm design
- 5.4. Simulations
- 5.5. Conclusion and notes
- References
- Chapter 6: Stochastic multi-energy management schemes with deferrable loads
- Abstract
- 6.1. Introduction
- 6.2. System model
- 6.3. Solution method
- 6.4. Performance analysis
- 6.5. Simulations
- 6.6. Conclusion and notes
- References
- Chapter 7: Joint design of energy trading and energy management for multiple IESs
- Abstract
- 7.1. Introduction
- 7.2. System model
- 7.3. Solution methodology
- 7.4. Performance analysis
- 7.5. Numerical results
- 7.6. Conclusion and notes
- References
- Appendix A: Optimization methods
- A.1. Distributionally robust optimization
- A.2. Online stochastic optimization
- A.3. Double-auction mechanism
- References
- Index
- No. of pages: 250
- Language: English
- Edition: 1
- Published: September 6, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780443141225
- eBook ISBN: 9780443141232
BY
Bo Yang
Bo Yang received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Hong Kong, in 2009. He is currently a Full Professor with Shanghai Jiao Tong University, Shanghai, China. Prior to joining Shanghai Jiao Tong University in 2010, he was a Post-Doctoral Researcher with the KTH Royal Institute of Technology, Stockholm, Sweden, from 2009 to 2010, and a Visiting Scholar with the Polytechnic Institute of New York University in 2007. His research interests include optimization for energy networks and internet of things. He has been the Principal Investigator in several research projects, including the NSFC Key Project. He was a recipient of the Ministry of Education Natural Science Award 2016, the Shanghai Technological Invention Award 2017, the Shanghai Rising- Star Program 2015, and the SMC-Excellent Young Faculty Award by Shanghai Jiao Tong University
Affiliations and expertise
Full Professor with Shanghai Jiao Tong University, Shanghai, China.ZW
Zhaojian Wang
Zhaojian Wang received the B.Sc. degree in electrical engineering from Tianjin University, Tianjin, China, in 2013, and the Ph.D. degree in electrical engineering from Tsinghua University, Beijing, China, in 2018. From 2016 to 2017, he was a Visiting Ph.D. Student with the California Institute of Technology, Pasadena, CA, USA. From 2018 to 2020, he was a Postdoctoral Scholar with Tsinghua University. He is currently an Assistant Professor with Shanghai Jiao Tong University, Shanghai, China. His research interests include stability analysis, optimal control, microgrid planning, and game theory-based decision making in energy and power systems. He is an Associate Editor for the IEEE Systems Journal and the IET Renewable Power Generation.
Affiliations and expertise
Assistant Professor, Department of Automation, Shanghai Jiao Tong University, ChinaXG
Xinping Guan
Xinping Guan received the B.Sc. degree in mathematics from Harbin Normal University, Harbin, China, in 1986, and the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 1999. He is currently the Chair Professor of Shanghai Jiao Tong University, Shanghai, China, where he is also the Dean of School of Electronic, Information and Electrical Engineering, and the Director of the Key Laboratory of Systems Control and Information Processing, Ministry of Education of China. Before that, he was the Executive Director of Office of Research Management, Shanghai Jiao Tong University, a Full Professor, and the Dean of School of Electrical Engineering, Yanshan University, Qinhuangdao, China. He has authored or co-authored five research monographs, more than 200 papers in IEEE transactions and other peer-reviewed journals, and numerous conference papers. His research interests include industrial network systems, smart manufacturing, and underwater networks. As a Principal Investigator, he has finished/been working on more than 20 national key projects. He is the Leader of the prestigious Innovative Research Team of the National Natural Science Foundation of China. He is an Executive Committee Member of Chinese Automation Association Council and the Chinese Artificial Intelligence Association Council. Dr. Guan was the recipient of the Second Prize of the National Natural Science Award of China in both 2008 and 2018, First Prize of Natural Science Award from the Ministry of Education of China in both 2006 and 2016, and IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2008. He is a National Outstanding Youth honored by NSF of China, and Changjiang Scholar's by the Ministry of Education of China and State-Level Scholar of New Century Bai Qianwan Talent Program of China. He is a Fellow of IEEE.
Affiliations and expertise
Chair Professor, Shanghai Jiao Tong University, Shanghai, ChinaRead Optimal Operation of Integrated Energy Systems Under Uncertainties on ScienceDirect