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  • Review Article
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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering volume 1, pages 163–179 (2024)Cite this article

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Abstract

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

Key points

  • The large variabilities in renewable energy (RE) generation can make it challenging for renewable power systems to provide stable power supplies; however, artificial intelligence (AI)-based methods can help overcome these challenges.

  • Deep learning methods can provide accurate RE generation forecasts to help balance the supply of and demand for electricity.

  • Reinforcement learning techniques can effectively handle the increased computational complexity associated with optimizing power dispatch for renewable power systems to ensure that costs are minimized and operational constraints are met.

  • Renewable power systems are subject to greater instabilities than traditional systems, which can lead to voltage and frequency fluctuations in the power supply. AI-based techniques can provide real-time control signals to facilitate generation-to-demand control.

  • Reinforcement learning techniques can also be used to analyse market behaviours and optimize decision-making to support the effective integration of RE into power markets.

  • Future AI-based methods will need to solve the challenges that could arise from increases in the number of entities supplying RE and the diversity of energy storage systems, which will further complicate renewable power systems.

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Fig. 1: A framework for renewable energy forecasts.
Fig. 2: The dispatch problem.
Fig. 3: The structure of AI-based control mechanisms for renewable power systems.
Fig. 4: The structure of an AI-based electricity market.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China (grant 2021ZD0201300), National Natural Science Foundation of China (grants 62325304 and 62073148) and Key Project of the National Natural Science Foundation of China (grant 62233006).

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Authors and Affiliations

  1. Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Yuanzheng Li, Fei Hu, Juntao Duan, Yong Zhao & Zhigang Zeng

  2. National Key Laboratory of Multispectral Information Intelligent Processing Technology, Huazhong University of Science and Technology, Wuhan, China

    Yuanzheng Li, Yong Zhao & Zhigang Zeng

  3. China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, China

    Yizhou Ding & Shangyang He

  4. Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China

    Guanghui Wen

  5. Department of Automation, Tsinghua University, Beijing, China

    Hua Geng

  6. State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zhengguang Wu

  7. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Hoay Beng Gooi

  8. School of Control Science and Engineering, Shandong University, Jinan, China

    Chenghui Zhang

  9. Department of Electrical Engineering, Tsinghua University, Beijing, China

    Shengwei Mei

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Contributions

Y.L., Y.D., S.H., F.H. and J.D. researched data for the article. All authors contributed substantially to discussion of the content. Y.L., Y.D., S.H., F.H., J.D., G.W. and H.B.G. wrote the article. All authors edited the manuscript before submission.

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Correspondence to Guanghui Wen, Hoay Beng Gooi or Zhigang Zeng.

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Li, Y., Ding, Y., He, S. et al. Artificial intelligence-based methods for renewable power system operation. Nat Rev Electr Eng 1, 163–179 (2024). https://doi.org/10.1038/s44287-024-00018-9

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