Development of Adaptive Power Dispatch Automation System Based on Artificial Intelligence and Machine Learning
DOI:
https://doi.org/10.13052/dgaej2156-3306.4154Keywords:
Power System Dispatching, Artificial Intelligence, Adaptive ControlAbstract
Faced with the challenges of high grid uncertainty, multi-timescale dynamic coupling, and operational efficiency bottlenecks caused by the high proportion of renewable energy integration, traditional dispatching methods struggle to achieve rapid response and global optimization. To address this issue, this study first constructs a joint perception model integrating graph attention networks and temporal convolutions to accurately extract the spatiotemporal dynamic features of the power grid. Next, a competitive collaborative decision-making framework based on multi-agent deep reinforcement learning is designed to achieve distributed collaborative control of power sources, grid, load, and storage. Then, a high-fidelity digital twin environment is introduced to perform online security verification and rolling optimization of agent strategies. Finally, a lightweight online update mechanism oriented towards concept drift is deployed. Experiments show that the developed AI-ADS (Artificial Intelligence-based Adaptive Dispatch System) increases the renewable energy absorption rate to 95.5%, reduces the dispatch response time to an average of 2.8 seconds, and lowers weekly operating costs by 21.5%, validating its effectiveness in improving grid resilience and economy through a “perception-decision-verification-evolution” intelligent closed loop.
Downloads
References
Hossain, M. A., Gray, E., Lu, J., et al. Optimized forecasting model to improve the accuracy of very short-term wind power prediction. IEEE Transactions on Industrial Informatics, 19(10): 10145–10159, 2023.
Lu, P., Ye, L., Pei, M., et al. Short-term wind power forecasting based on meteorological feature extraction and optimization strategy. Renewable Energy, 184(1): 642–661, 2022.
Gao, X., Guo, W., Mei, C., et al. Short-term wind power forecasting based on SSA-VMD-LSTM. Energy Reports, 9(1): 335–344, 2023.
He, B., Ye, L., Pei, M., et al. A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. Energy Reports, 8(3): 929–939, 2022.
Tian, X., Gu, N., Huang, X., et al. Progress on short-term wind power forecasting technology. Journal of Mechanical Engineering, 58(12): 213–236, 2022.
Muttaqi, K. M., and Sutanto, D. Adaptive and predictive energy management strategy for real-time optimal power dispatch from virtual power plants integrated with renewable energy and energy storage. IEEE Transactions on Industry Applications, 57(3): 1958–1972, 2021.
Niu, M., Xu, N. Z., Dong, H. N., et al. Adaptive range composite differential evolution for fast optimal reactive power dispatch. IEEE Access, 9: 20117–20126, 2021.
Gao, N., Gao, D. W., and Fang, X. Manage real-time power imbalance with renewable energy: Fast generation dispatch or adaptive frequency regulation? IEEE Transactions on Power Systems, 38(6): 5278–5289, 2022.
Odonkor, E. N., Moses, P. M., and Akumu, A. O. Intelligent ANFIS-based distributed generators energy control and power dispatch of grid-connected microgrids integrated into distribution networks. International Journal of Electrical and Electronic Engineering & Telecommunications, 13(2): 112–124, 2024.
Tang, H., Lv, K., Bak-Jensen, B., et al. Deep neural network-based hierarchical learning method for dispatch control of multi-regional power grid. Neural Computing and Applications, 34(7): 5063–5079, 2022.
Ning, C., and You, F. Deep learning-based distributionally robust joint chance constrained economic dispatch under wind power uncertainty. IEEE Transactions on Power Systems, 37(1): 191–203, 2021.
Guo, F., Xu, B., Xing, L., et al. An alternative learning-based approach for economic dispatch in smart grids. IEEE Internet of Things Journal, 8(19): 15024–15036, 2021.
Shibl, M. M., Ismail, L. S., and Massoud, A. M. An intelligent two-stage energy dispatch management system for hybrid power plants: Impact of machine learning deployment. IEEE Access, 11: 13091–13102, 2023.
Fang, X., and Khazaei, J. A two-stage deep learning approach for solving microgrid economic dispatch. IEEE Systems Journal, 17(4): 6237–6247, 2023.
Dong, W., Yang, Q., Li, W., et al. Machine-learning-based real-time economic dispatch in islanding microgrids in a cloud-edge computing environment. IEEE Internet of Things Journal, 8(17): 13703–13711, 2021.
Yin, Z., Zhang, X., Luo, W., and Ruan, S. Scalable Power Dispatch Automation Methods Based on Artificial Intelligence in Distributed Energy Systems. Distributed Generation & Alternative Energy Journal, 41(03): 791–814, 2026.
Li, J., Xie, Y., Ma, W., and Huang, K. Research on Intelligent Control Technology for Cooperative Game Implementation in Source-Grid-Load-Storage Systems Based on Reinforcement Learning. Distributed Generation & Alternative Energy Journal, 41(02): 387–432, 2026.
Wan, Y., Long, C., Deng, R., et al. Adaptive event-triggered strategy for economic dispatch in uncertain communication networks. IEEE Transactions on Control of Network Systems, 8(4): 1881–1891, 2021.
Li, P., Wu, Z., Zhang, C., et al. Multi-timescale affinely adjustable robust reactive power dispatch of distribution networks integrated with high penetration of photovoltaic systems. Journal of Modern Power Systems and Clean Energy, 11(1): 324–334, 2021.
Mou, J., Duan, P., Gao, L., et al. Biologically inspired machine learning-based trajectory analysis in intelligent dispatching energy storage systems. IEEE Transactions on Intelligent Transportation Systems, 24(4): 4509–4518, 2022.
