
Optimal Operation of Integrated Energy Systems Under Uncertainties
Distributionally Robust and Stochastic Methods
- 1st Edition - September 6, 2023
- Imprint: Elsevier
- 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

Purchase options

Institutional subscription on ScienceDirect
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
- 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
- Edition: 1
- Published: September 6, 2023
- Imprint: Elsevier
- No. of pages: 250
- Language: English
- Paperback ISBN: 9780443141225
- eBook ISBN: 9780443141232
BY
Bo Yang
ZW
Zhaojian Wang
XG