The Application Progress of Artificial Intelligence Technology in The Optimal Dispatch of Smart Grids

Jiahao Bai

doi:10.54097/sxfkv686

Authors

Jiahao Bai School of Information and Intelligence Engineering, Tianjin Renai College, Tianjin,301600 China

DOI:

https://doi.org/10.54097/sxfkv686

Keywords:

Artificial Intelligence; Smart Grid; Optimal Dispatch; Energy storage scheduling; New energy consumption.

Abstract

With a high proportion of new energy sources connected to the power grid, the traditional dispatching model is facing severe challenges in dealing with uncertainties.To address this challenge, this article systematically reviews the progress of the application of artificial intelligence(AI) technology in the optimization scheduling of smart grids..The research focuses on three core methods: data-driven prediction, multi-objective optimization decision-making, and real-time perception diagnosis.Case studies have shown that artificial intelligence technology can systematically enhance the operational efficiency of power grids. In scenarios such as new energy grid connection, energy storage scheduling, and safety management, it has achieved significant reductions in the rate of wind power abandonment and a qualitative improvement in the efficiency of fault handling.The research has verified the crucial role of artificial intelligence technology in facilitating the transformation of power grid dispatching from "passive response" to "active optimization", and has provided a technical path and empirical basis for building an efficient and reliable new power system.

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References

[1] Ye H Y, Deng S, Li Z P, et al. “Exploration of Carbon Accounting and Dual-Control Mechanism for Carbon Emissions in Power Grid Enterprises Under the ‘Dual Carbon’ Goals” [J]. Power Big Data, 2025, 28(04): 1-12.

[2] S. E. Haupt, T. C. McCandless, S. Dettling, S. Alessandrini, J. A. Lee, S. Linden, W. Petzke, T. Brummet, N. Nguyen, B. Kosovic, G. Wiener, T. Hussain, and M. Al-Rasheedi, “Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting,” Energies, vol. 13, no. 8, p. 1979, Apr. 2020.

[3] S. Sankarananth, M. Karthiga, and D. P. Bavirisetti, “AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids,” Energy Rep., vol. 10, pp. 1299–1312, Nov. 2023.

[4] Tian S, Guo S Q. Deep Reinforcement Learning-Based Measurement and Optimization Strategy for Electricity Demand Response [J]. Industrial Control Computer, 2025, 38(10): 145+148.

[5] Li Q. Novel Multi-Region Load Forecasting Research for Power Systems Based on Federated Spatio-Temporal Graph Neural Networks [J]. Hongshui River, 2025, 44(04): 101-107.

[6] Paruk Aimaiti, Luo W, Li X J. Application of Intelligent Algorithms in Electrical System Optimization [J]. Integrated Circuit Application, 2024, 41(11): 184-185.

[7] 《China Renewable Energy Development Report 2023》 Released [J]. Energy Conservation and Environmental Protection, 2024, (07): 1.

[8] Chen, Z. Y., Yu, T., Ding, M. S., et al. A Spatio-Temporal Convolutional Dynamic Knowledge Graph-Based Method for Evaluating New Energy Integration[J]. Automation of Electric Power Systems, 47(15), 46-54.

[9] Shahid, A., Plaum, F., Korõtko, T., & Rosin, A. (2024). AI Technologies and Their Applications in Small-Scale Electric Power Systems. IEEE Access, 12.

[10] Yang, R. F., Han, Y. Peak-shaving control strategy for grid-side energy storage systems based on an improved deep reinforcement learning algorithm [J/OL]. Energy Storage Science and Technology, 1-10 2025-10-24

[11] L. Driscoll, S. de la Torre, and J. A. Gomez-Ruiz, “Feature-based lithium-ion battery state of health estimation with artificial neural networks,” J. Energy Storage, vol. 50, Jun. 2022, Art. no. 104584

[12] Zhang M, Li M, Wang J P, et al. Optimization of Grid Security and Stability Operation Models Based on Artificial Intelligence [J]. Electrical Technology and Economics, 2025, (04): 264-266.