With the severe trilemma of resource depletion, ecosystem degradation, and environmental pollution, strategies of energy conservation and emission reduction are the optimal routes to realizing sustainable development. A “smart energy” approach is now an inevitable link to achieving energy conservation and emission reduction policies and ensuring sustainable development. Compared with traditional single-energy systems, smart energy systems emphasize coupling among heterogeneous energy systems. This high degree of coupling enables smart energy systems to meet complex patterns of energy demand, but at the same time, brings new challenges in coordinating power management, optimizing operation and maintenance costs, and determining operating status. Therefore, research on advanced technology and equipment is expected to improve the economy and sustainability of smart energy systems, which is of great significance for achieving high efficient energy utilization.
The following research goals have been identified:
1) Apply artificial intelligence-based methods to achieve smart planning and operation of the smart grid.
2) Fully integrate and mobilize various energy resources such as electric energy, heat energy, and natural gas between regions in the smart grids.
3) Make full use of the complementary characteristics of each energy resource in time and/or space to meet the energy use demand in the region.
4) Realize coordination among different energy subsystems to improve energy efficiency and reduce carbon emissions.
In line with the "Climate Action" and "Life on Land" in the Sustainable Development Goals of the United Nations, this Research Topic welcomes original research articles and reviews. Research areas may include (but are not limited to) the following:
• Electric power system planning
• Electric power system dispatching
• Smart grid control
• Integrated energy system resilience
• Renewable energy integration
• Power electronic technology
• Active distribution networks
• Prediction and regulation of energy systems
• Artificial intelligence technology
• Machine learning and deep learning
With the severe trilemma of resource depletion, ecosystem degradation, and environmental pollution, strategies of energy conservation and emission reduction are the optimal routes to realizing sustainable development. A “smart energy” approach is now an inevitable link to achieving energy conservation and emission reduction policies and ensuring sustainable development. Compared with traditional single-energy systems, smart energy systems emphasize coupling among heterogeneous energy systems. This high degree of coupling enables smart energy systems to meet complex patterns of energy demand, but at the same time, brings new challenges in coordinating power management, optimizing operation and maintenance costs, and determining operating status. Therefore, research on advanced technology and equipment is expected to improve the economy and sustainability of smart energy systems, which is of great significance for achieving high efficient energy utilization.
The following research goals have been identified:
1) Apply artificial intelligence-based methods to achieve smart planning and operation of the smart grid.
2) Fully integrate and mobilize various energy resources such as electric energy, heat energy, and natural gas between regions in the smart grids.
3) Make full use of the complementary characteristics of each energy resource in time and/or space to meet the energy use demand in the region.
4) Realize coordination among different energy subsystems to improve energy efficiency and reduce carbon emissions.
In line with the "Climate Action" and "Life on Land" in the Sustainable Development Goals of the United Nations, this Research Topic welcomes original research articles and reviews. Research areas may include (but are not limited to) the following:
• Electric power system planning
• Electric power system dispatching
• Smart grid control
• Integrated energy system resilience
• Renewable energy integration
• Power electronic technology
• Active distribution networks
• Prediction and regulation of energy systems
• Artificial intelligence technology
• Machine learning and deep learning