Modeling and Optimization of Power Grid Intelligent Restoration Strategy Combining Diffusion Models and Reinforcement Learning
Main Article Content
Abstract
With the increasing proportion of renewable energy integration in modern power systems, grid fault recovery faces greater complexity and uncertainty, posing significant challenges for intelligent energy transmission infrastructures and reliable operation in electromagnetic power networks. To address these issues, this paper proposes a collaborative decision-making model (CDM-RL) that combines diffusion models (DM) and reinforcement learning (RL) for intelligent power grid restoration. The proposed framework generates physically feasible fault scenarios through a denoising diffusion probabilistic model (DDPM) and integrates graph neural networks (GNN) with proximal policy optimization (PPO) to achieve efficient restoration control under complex operating conditions. An alternating training mechanism is further introduced to enhance the generalization capability of the model across diverse fault scenarios. Experimental evaluation on the IEEE 33-bus system demonstrates that CDM-RL significantly outperforms conventional approaches, achieving an initial power restoration time of 8.9 s, a load recovery rate of 93.1%, and an average of only 3.9 switching operations while maintaining superior cross-scenario stability of 85.3%. The proposed AI-driven framework improves grid self-healing capability and provides an effective technical pathway for enhancing the resilience and operational reliability of intelligent electromagnetic energy transmission and distribution systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
T. Egeland-Eriksen, A. Hajizadeh, and S. Sartori, “Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives,” International journal of hydrogen energy, vol. 46, no. 63, pp. 31963-31983, 2021, doi: 10.1016/j.ijhydene.2021.06.218.
Y. Shu, G. Chen, J. He, and F. Zhang, “Building a new electric power system based on new energy sources,” Strategic Study of Chinese Academy of Engineering, vol. 23, no. 6, pp. 61-69, 2021, doi: 10.15302/J-SSCAE-2021.06.003.
H. Zhang, W. Xiang, W. Lin, and J. Wen, “Grid forming converters in renewable energy sources dominated power grid: Control strategy, stability, application, and challenges,” Journal of modern power systems and clean energy, vol. 9, no. 6, pp. 1239-1256, 2021, doi: 10.35833/MPCE.2021.000257.
Q. Li, B. Ren, W. Tang, D. Wang, C. Wang, and Z. Lv, “Analyzing the inertia of power grid systems comprising diverse conventional and renewable energy sources,” Energy Reports, vol. 8, pp. 15095-15105, 2022, doi: 10.1016/j.egyr.2022.11.022.
C. D. Iweh, S. Gyamfi, E. Tanyi, and E. Effah-Donyina, “Distributed generation and renewable energy integration into the grid: Prerequisites, push factors, practical options, issues and merits,” Energies, vol. 14, no. 17, pp. 5375-5408, 2021, doi: 10.3390/en14175375.
A. A. Kebede, T. Kalogiannis, J. Van Mierlo, and M. Berecibar, “A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration,” Renewable and sustainable energy re-views, vol. 159, pp. 112213-112231, 2022, doi: 10.1016/j.rser.2022.112213.
Q. Liu, V. Hagenmeyer, and H. B. Keller, “A review of rule learning-based intrusion detection systems and their pro-spects in smart grids,” Ieee Access, vol. 9, pp. 57542-57564, 2021, doi: 10.1109/ACCESS.2021.3071263.
J. Yan, B. Hu, C. Shao, W. Huang, Y. Sun, W. Zhang, et al., “Scheduling post-disaster power system repair with in-complete failure information: A learning-to-rank approach,” IEEE Transactions on Power Systems, vol. 37, no. 6, pp. 4630-4641, 2022, doi: 10.1109/TPWRS.2022.3149983.
D. Liu, X. Zhang, and K. T. Chi, “Effects of high level of penetration of renewable energy sources on cascading failure of modern power systems,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 12, no. 1, pp. 98-106, 2022, doi: 10.1109/JETCAS.2022.3147487.
W. Strielkowski, L. Civín, E. Tarkhanova, M. Tvaronavičienė, and Y. Petrenko, “Renewable energy in the sustainable development of electrical power sector: A review,” Energies, vol. 14, no. 24, pp. 8240-8263, 2021, doi: 10.3390/en14248240.
J. Hu, X. Liu, M. Shahidehpour, and S. Xia, “Optimal operation of energy hubs with large-scale distributed energy resources for distribution network congestion management,” IEEE Transactions on Sustainable Energy, vol. 12, no. 3, pp. 1755-1765, 2021, doi: 10.1109/TSTE.2021.3064375.
L. Lv, Z. Wu, J. Zhang, L. Zhang, Z. Tan, and Z. Tian, “A VMD and LSTM based hybrid model of load forecasting for power grid security,” IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6474-6482, 2021, doi: 10.1109/TII.2021.3130237.
C. Wang, P. Ju, F. Wu, X. Pan, and Z. Wang, “A systematic review on power system resilience from the perspective of generation, network, and load,” Renewable and Sustainable Energy Reviews, vol. 167, pp. 112567-112587, 2022, doi: 10.1016/j.rser.2022.112567.
P. Denholm, D. J. Arent, S. F. Baldwin, D. E. Bilello, G. L. Brinkman, J. M. Cochran, et al., “The challenges of achieving a 100% renewable electricity system in the United States,” Joule, vol. 5, no. 6, pp. 1331-1352, 2021, doi: 10.1016/j.joule.2021.03.028.
A. M. Nassef, M. A. Abdelkareem, H. M. Maghrabie, and A. Baroutaji, “Review of metaheuristic optimization algo-rithms for power systems problems,” Sustainability, vol. 15, no. 12, pp. 9434-9460, 2023, doi: 10.3390/su15129434.
M. Mahdavi, H. H. Alhelou, A. Bagheri, S. Z. Djokic, and R. A. V. Ramos, “A comprehensive review of metaheuristic methods for the reconfiguration of electric power distribution systems and comparison with a novel approach based on efficient genetic algorithm,” IEEE Access, vol. 9, pp. 122872-122906, 2021, doi: 10.1109/ACCESS.2021.3109247.
S. A. Shezan, M. F. Ishraque, G. M. Shafiullah, I. Kamwa, L. C. Paul, S. M. Muyeen, et al., “Optimization and control of solar-wind islanded hybrid microgrid by using heuristic and deterministic optimization algorithms and fuzzy logic controller,” Energy reports, vol. 10, pp. 3272-3288, 2023, doi: 10.1016/j.egyr.2023.10.016.
X. Ding, X. Liao, W. Cui, X. Meng, R. Liu, Q. Ye, et al., “A Deep Reinforcement Learning Optimization Method Con-sidering Network Node Failures,” Energies, vol. 17, no. 17, pp. -4483, 2024, doi: 10.3390/en17174471.
M. M. Hosseini and M. Parvania, “Resilient operation of distribution grids using deep reinforcement learning,” IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2100-2109, 2021, doi: 10.1109/TII.2021.3086080.
W. Liao, B. Bak-Jensen, J. R. Pillai, Y. Wang, and Y. Wang, “A review of graph neural networks and their applications in power systems,” Journal of Modern Power Systems and Clean Energy, vol. 10, no. 2, pp. 345-360, 2021, doi: 10.35833/MPCE.2021.000058.
L. Pinciroli, P. Baraldi, G. Ballabio, M. Compare, and E. Zio, “Deep reinforcement learning based on proximal policy optimization for the maintenance of a wind farm with multiple crews,” Energies, vol. 14, no. 20, pp. 6743-6759, 2021, doi: 10.3390/en14206743.
B. Er-Rahmadi and T. Ma, “Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems,” European Journal of Operational Research, vol. 303, no. 1, pp. 337-353, 2022, doi: 10.1016/j.ejor.2022.02.006.