Low-Carbon Oriented Improvement Strategy for Flexibility and Resiliency of Multi-Energy Systems

Due to the inherent volatility and randomness, the increasing share of energy from renewable resources presents a challenge to the operation of multi-energy systems with heterogeneous energy carriers such as electricity, heat, hydrogen, etc. These factors will make the systems hard to adjust their supply and demand flexibly to maintain energy balance to ensure reliability. Further, this hinders the development of a low-carbon and economically viable energy system. By making full use of the synergistic interaction of generation, transmission, load demand, and energy storage, a three-fold approach focused on quantifying demand flexibility, evaluating supply capabilities, and enhancing resilience can unlock the flexibility potential across various sectors of new energy systems. This approach provides an effective means of facilitating the transition from conventional energy systems to low-carbon, clean-energy-oriented paradigms. However, huge challenges arising from renewable energy pose great obstacles to the aforementioned solution pathway.

The main objectives of this Research Topic are：
1. Develop advanced carbon emission accounting and measurement techniques for emerging multi-energy systems
2. Design effective methods for predicting renewable electricity generation
3. Proposed efficient methods for quantitative assessment of uncertainty from renewables and loads
4. Put forward advanced evaluation, optimization, and planning strategies incorporating diverse flexibility resources
5. Design multifaceted market mechanisms and collaborative frameworks balancing economics and low carbon footprint
6. Develop operational control and resilience-enhancement techniques for distribution networks under large-scale distributed energy integration

Topics to be covered include, but are not limited to:
• Carbon emission quantification methods for multi-power systems
• Application of artificial intelligence technologies in supply and load forecasting
• Electricity-carbon emission coupling mechanism in the multi-market framework
• Planning, dispatching, and resilience improvement of new power systems
• Mechanisms for diverse demand response in new power systems
• Coordinated optimization and scheduling of transmission and distribution
• Uncertainty handling methods for renewable energy sources
• Reinforcement learning and artificial intelligence algorithms
• Collaborative operation optimization of distribution networks under large-scale distributed energy integration
• Intelligent fault location and recovery in smart distribution grids
• Voltage stability assessment theory and applications