Zhaojian Wang – författare

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

Merging Optimization and Control in Power Systems A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions In Merging Optimization and Control in Power Systems an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates. This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments. Readers will also find: A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the bookComprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demandData, tables, illustrations, and case studies covering realistic power systems and experimentsIn-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed modelPerfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, Merging Optimization and Control in Power Systems is an advanced and timely treatment of distributed optimal controller design.

Merging Optimization and Control in Power Systems

A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions

In Merging Optimization and Control in Power Systems an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates.

This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments.

Readers will also find:

A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the book Comprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demand Data, tables, illustrations, and case studies covering realistic power systems and experiments In-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed model

Perfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, Merging Optimization and Control in Power Systems is an advanced and timely treatment of distributed optimal controller design.