Optimal 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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Day-ahead energy management of IES with a distributionally robust approach - p. 37 3. Chapter 3 Distributionally robust heat-and-electricity pricing for IES with decision-dependent uncertainties - p. 63 4. Chapter 4 Multi-level coordinated energy management for IES in hybrid markets - p. 85 5. Chapter 5 Energy management based on multi-agent deep reinforcement learning for IES - p. 121 6. Chapter 6 Stochastic multi-energy management schemes with deferrable loads - p. 147 7. Chapter 7 Joint design of energy trading and energy management for multiple IESs - p. 171 8. Appendix A Optimization methods - p. 205 9. Index - p. 233
Optimal 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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Day-ahead energy management of IES with a distributionally robust approach - p. 37 3. Chapter 3 Distributionally robust heat-and-electricity pricing for IES with decision-dependent uncertainties - p. 63 4. Chapter 4 Multi-level coordinated energy management for IES in hybrid markets - p. 85 5. Chapter 5 Energy management based on multi-agent deep reinforcement learning for IES - p. 121 6. Chapter 6 Stochastic multi-energy management schemes with deferrable loads - p. 147 7. Chapter 7 Joint design of energy trading and energy management for multiple IESs - p. 171 8. Appendix A Optimization methods - p. 205 9. Index - p. 233
