Smart Robust Operation and Trading of Integrated Energy Systems with Low Pollution Goals

Smart Robust Operation and Trading of Integrated Energy Systems with Low Pollution Goals
Author :
Publisher : Frontiers Media SA
Total Pages : 320
Release :
ISBN-13 : 9782832554685
ISBN-10 : 2832554687
Rating : 4/5 (87 Downloads)

Book Synopsis Smart Robust Operation and Trading of Integrated Energy Systems with Low Pollution Goals by : Kaiping Qu

Download or read book Smart Robust Operation and Trading of Integrated Energy Systems with Low Pollution Goals written by Kaiping Qu and published by Frontiers Media SA. This book was released on 2024-09-23 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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