AI4S: Artificial Intelligence Empowering the Development of New Power Systems
With the continuous growth of global energy demand and the transformation of the energy mix, the power system is facing unprecedented challenges, including the integration of renewable energy, the intelligent upgrading of the grid, and the enhancement of power supply reliability. Against this backdrop, this paper discusses the challenges faced by the power system with the integration of renewable energy and the widespread adoption of power electronic devices, emphasizing the significant role of AI for Science in addressing these challenges. Then, it provides a detailed analysis of the specific applications of AI for Science in three typical application scenarios of the power system: time-series forecasting, microgrid optimization, and distribution network control, demonstrating its remarkable effects in improving prediction accuracy, optimizing scheduling strategies, and enhancing system reliability. Finally, the article summarizes the application achievements of AI for Science in the power system and looks forward to its future development trends and application prospects in power system modeling, scheduling, and control, indicating that AI for Science will continue to drive the development of the power system towards intelligence, efficiency, and safety. In the future, with the continuous advancement of technology and the expansion of application scenarios, AI for Science will play an even more critical role in the power system. It will assist the power system in better adapting to the volatility and uncertainty of new energy, promote the integration of the power system with emerging fields such as the Energy Internet and smart cities, and provide strong technical support for building a cleaner, more efficient, and more reliable energy system.
Authors
- Tianlu Gao (ORCID: https://orcid.org/0000-0003-0625-7002)
- Sheng Chen (ORCID: https://orcid.org/0000-0003-3519-1137)
- Qi Dong Zhao (ORCID: https://orcid.org/0000-0001-9713-1864)
- Jun Zhang (ORCID: https://orcid.org/0000-0002-5213-0857)
- Huan Ma
- Yuyang Bai (ORCID: https://orcid.org/0000-0001-6261-4583)
- Wanya Yu
- Haozhe Zhan
- Xinying Wang (ORCID: https://orcid.org/0009-0000-8928-9282)
Institutions
- North China Electric Power University (CN)
- State Grid Corporation of China (China) (CN)
- Wuhan University (CN)
- China Electric Power Research Institute
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/icips64173.2024.10900144
- Primary Topic
- Electric Power Systems and Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00