Artificial Intelligence for Smart Grids and Sustainable Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 10 August 2026 | Viewed by 258
Special Issue Editor
Interests: renewable energy output prediction; deep learning; convolutional neural network methods; photovoltaic systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Under the backdrop of the "dual carbon" goal and the accelerated construction of the new power system, the high proportion of renewable energy grid connection and the multi-energy flow coupling interaction have significantly increased the complexity of grid operation. Moreover, the frequent occurrence of extreme disaster events has further exacerbated the risks to the safe and stable operation of the grid, placing higher demands on the grid's disaster perception, disaster resilience, emergency rescue, and data processing capabilities. China Southern Power Grid, as the core carrier of domestic energy supply security and a benchmark for international cross-border energy interconnection, the complexity of the terrain and the diversity of the climate in its coverage areas further highlight the urgency of the practical necessity for the grid to resist disasters and mitigate risks, and for efficient scheduling. To overcome the bottlenecks of traditional technologies such as insufficient accuracy in grid disaster perception, one-sided resilience assessment, and inefficient data fusion, it is urgent to rely on cutting-edge technologies such as quantum computing, deep learning, reinforcement learning, and multi-source data fusion to innovate theoretical methods and technical paths for grid disaster prevention and control, resilience enhancement, and intelligent scheduling. In this context, this Special Issue focuses on the core requirements for the safe and stable operation of the new power system and integrated energy system and focuses on collecting innovative research results in areas such as grid disaster perception, disaster resilience assessment, emergency data processing, and intelligent scheduling optimization. It provides an academic exchange platform and technical support to promote the grid towards higher resilience, higher intelligence, and higher efficiency.
- Grid disaster perception; Multi-source data fusion; Quantum deep learning; Dempster-Shafer theory; Distribution grid
- Grid disaster resilience; Key component assessment; Disaster response
- Quantum graph deep neural network; Grid topology; Vulnerability assessment; Boundary value analysis
- Deep reinforcement learning; Automatic update of network parameters; Multi-link data fusion; Real-time scheduling
- Reinforcement learning; Deep reinforcement learning; New power system; Integrated energy system; Active/ reactive power control; Stability control
- High proportion of renewable energy; Power system dispatching; Unit commitment; Reactive power optimization
- Deep learning; Acceleration of power system optimization
- Large model; New power system; Integrated energy system.
Dr. Linfei Yin
Guest Editor
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Keywords
- power grid disaster perception
- quantum heuristic algorithm
- power grid disaster resilience
- quantum graph deep neural network
- lightweight deep convolutional neural network
- deep reinforcement learning
- reinforcement learning
- high proportion of renewable energy
- deep learning
- large model
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