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.- 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

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.- 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

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.- 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