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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 Multi-Energy Systems Under Uncertainty discusses core concepts, advanced modeling and key operation strategies for integrated multi-energy systems geared for use in optimal operation. The book particularly focuses on reviewing novel operating strategies supported by relevant code in MATLAB and GAMS. It covers foundational concepts, key challenges and opportunities in operational implementation, followed by discussions of conventional approaches to modeling electricity, heat and gas networks. This modeling is the base for more detailed operation strategies for optimal operation of integrated multi-energy systems under uncertainty covered in the latter part of the work. - Reviews advanced modeling approaches relevant to the integration of electricity, heat and gas systems in operation studies - Covers stochastic and robust optimal operation of integrated multi-energy systems - Evaluates MPC based, real-time dispatch of integrated multi-energy systems - Considers uncertainty modeling for stochastic and robust optimization - Assesses optimal operation and real-time dispatch for multi-energy building complexes
To mitigate two major environmental concerns of global warming and air pollution, renewable energies with uncertainty are increasingly deployed in power systems, which challenge the system's secure operation. A single system usually has limited adjusting ability. In contrast, integrated energy systems such as electricity-gas, electricity-traffic, electricity-heat, and transmission-distribution coordinated systems enhance the regulating ability of renewable energy accommodation and environmental protection. The operation of integrated energy systems will meet three essential requirements: low-pollution attribute, robustness, and cooperativity. However, the diversity of uncertainty conditions, the complementarity of new energy accommodation among systems, the conflict of interest between systems, and the dispatch autonomy of systems challenge the requirements mentioned above. The main goal of this Research Topic includes: 1. Propose more effective trading mechanisms or control strategies for carbon and air pollutant emissions. 2. Fully use complementary effects between electric power, natural gas, heat, hydrogen, and traffic systems. 3. Realize the coordinated operation of integrated energy systems with limited information interaction and ensured dispatch autonomy. 4. Improve the robustness of integrated energy systems under diversified uncertainty conditions. 5. Apply data-based reinforcement learning methods for the dynamic decision of smart integrated energy systems under complex environments.
The 3-volume set CCIS 1586, CCIS 1587 and CCIS 1588 constitutes the refereed proceedings of the 8th International Conference on Artificial Intelligence and Security, ICAIS 2022, which was held in Qinghai, China, in July 2022. The total of 115 full papers and 53 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1124 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; big data; cloud computing and security; multimedia forensics; Part III: encryption and cybersecurity; information hiding; IoT security.
This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration. It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling. Key Features: Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers. Discusses dependence modeling of multi-site renewable generations in detail. Covers probabilistic analysis, useful for data analysts. Discusses various aspects of renewable energy integration i.e. technical, economic, etc. Covers correlation factors, and methodologies are validated with case studies with various standard test systems. The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.
This book is a printed edition of the Special Issue "Advances in Integrated Energy Systems Design, Control and Optimization" that was published in Applied Sciences