With the increasing number of extreme events like natural disasters, man-made accidents, and deliberate attacks, the reliability and economic operation of multi-energy system such as multi-energy microgrid, multi-energy ships, etc., can be severely damaged. This Research Topic deals with the data-based resilience of multi-energy systems, studies this problem from the system modelling and operation perspective. In particular, multi-energy systems with the coordination of different energy networks such as power, gas, heating, cooling, water, etc., which can be challenged by natural disasters such as hurricanes, and floods, with huge loss of generation and loads will be investigated.
The main objective of this Research Topic is to provide cost-effective, coordinated, and resilience oriented operation measures for currently emerging multi-energy systems. Firstly, the system modelling is comprehensively developed considering the intensive coordination of heterogeneous energy, then, a resilient scheme is designed to alleviate the negative effects of extreme events. Then, the data-based deep reinforcement learning (DRL) algorithms will be implemented to provide online smart operation solutions with massive historical data and unknown attack information. Finally, uncertainty sources which would pose a great threat to the stable and reliable system operation will be addressed.
Topics to be covered include, but are not limited to:
- Development of intelligent forecasting methods for extreme events
- Modelling outcomes from various disasters on the multi-energy systems
- Multi-energy support modelling of all different sectors
- Data based methods development for resilience of multi-energy systems
- Uncertainty handling for the resilient operation of multi-energy systems
With the increasing number of extreme events like natural disasters, man-made accidents, and deliberate attacks, the reliability and economic operation of multi-energy system such as multi-energy microgrid, multi-energy ships, etc., can be severely damaged. This Research Topic deals with the data-based resilience of multi-energy systems, studies this problem from the system modelling and operation perspective. In particular, multi-energy systems with the coordination of different energy networks such as power, gas, heating, cooling, water, etc., which can be challenged by natural disasters such as hurricanes, and floods, with huge loss of generation and loads will be investigated.
The main objective of this Research Topic is to provide cost-effective, coordinated, and resilience oriented operation measures for currently emerging multi-energy systems. Firstly, the system modelling is comprehensively developed considering the intensive coordination of heterogeneous energy, then, a resilient scheme is designed to alleviate the negative effects of extreme events. Then, the data-based deep reinforcement learning (DRL) algorithms will be implemented to provide online smart operation solutions with massive historical data and unknown attack information. Finally, uncertainty sources which would pose a great threat to the stable and reliable system operation will be addressed.
Topics to be covered include, but are not limited to:
- Development of intelligent forecasting methods for extreme events
- Modelling outcomes from various disasters on the multi-energy systems
- Multi-energy support modelling of all different sectors
- Data based methods development for resilience of multi-energy systems
- Uncertainty handling for the resilient operation of multi-energy systems