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

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Kaiping Qu

China University of Mining and Technology

Xin Fang

Mississippi State University

Yue Zhou

Cardiff University

Shuli Wen

Shanghai Jiao Tong University

Shiwei Xie

Fuzhou University

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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.

Topics to be covered include, but are not limited to:
• Coordinated optimization of integrated energy systems and transmission-distribution systems.
• Control strategies for carbon emissions and air pollutant emissions.
• Demand response in the active distribution network.
• Distributed optimization of interconnected systems.
• Uncertainty handling of renewable energies and demand.
• Trading mechanisms in energy markets.
• Reinforcement learning and other artificial intelligence methods.

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Editors

Kaiping Qu

China University of Mining and Technology

Xin Fang

Mississippi State University

Yue Zhou

Cardiff University

Shuli Wen

Shanghai Jiao Tong University

Shiwei Xie

Fuzhou University

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29,171 Article views

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1,558 Topic views

Published In

Frontiers in Energy Research

Process and Energy Systems Engineering

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