Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

High-Speed Voltage Control in Active Distribution Systems with Smart Inverter Coordination and Deep Reinforcement Learning (2311.13080v2)

Published 22 Nov 2023 in eess.SY and cs.SY

Abstract: The increasing penetration of renewable energy resources in distribution systems necessitates high-speed monitoring and control of voltage for ensuring reliable system operation. However, existing voltage control algorithms often make simplifying assumptions in their formulation, such as real-time availability of smart meter measurements (for monitoring), or real-time knowledge of every power injection information(for control).This paper leverages the recent advances made in highspeed state estimation for real-time unobservable distribution systems to formulate a deep reinforcement learning-based control algorithm that utilizes the state estimates alone to control the voltage of the entire system. The results obtained for a modified (renewable-rich) IEEE34-nodedistributionfeeder indicate that the proposed approach excels in monitoring and controlling voltage of active distribution systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (21)
  1. A. Amanipoor, M. S. Golsorkhi, N. Bayati, and M. Savaghebi, “V-Iq based control scheme for mitigation of transient overvoltage in distribution feeders with high PV penetration,” IEEE Transactions on Sustainable Energy, vol. 14, no. 1, pp. 283–296, 2022.
  2. S. Singh, S. Veda, S. P. Singh, R. Jain, and M. Baggu, “Event-driven predictive approach for real-time Volt/VAr control with CVR in solar PV rich active distribution network,” IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 3849–3864, 2021.
  3. A. Suresh, R. Bisht, and S. Kamalasadan, “A coordinated control architecture with inverter-based resources and legacy controllers of power distribution system for voltage profile balance,” IEEE Transactions on Industry Applications, vol. 58, no. 5, pp. 6701–6712, 2022.
  4. Y. Li and J. Yan, “Cybersecurity of smart inverters in the smart grid: A survey,” IEEE Transactions on Power Electronics, 2022.
  5. Z. Tang, D. J. Hill, and T. Liu, “Distributed coordinated reactive power control for voltage regulation in distribution networks,” IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 312–323, 2020.
  6. C. Zhang, Y. Xu, Y. Wang, Z. Y. Dong, and R. Zhang, “Three-stage hierarchically-coordinated voltage/VAr control based on PV inverters considering distribution network voltage stability,” IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 868–881, 2021.
  7. D. Dalal, M. Sondharangalla, R. Ayyanar, and A. Pal, “Improving photovoltaic hosting capacity of distribution networks with coordinated inverter control – a case study of the EPRI J1 feeder,” 2023. [Online]. Available: https://arxiv.org/abs/2311.02793
  8. Y. Zhang, X. Wang, J. Wang, and Y. Zhang, “Deep reinforcement learning based Volt-VAr optimization in smart distribution systems,” IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 361–371, 2020.
  9. D. Cao, J. Zhao, W. Hu, F. Ding, Q. Huang, Z. Chen, and F. Blaabjerg, “Data-driven multi-agent deep reinforcement learning for distribution system decentralized voltage control with high penetration of PVs,” IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 4137–4150, 2021.
  10. J. Wang, W. Xu, Y. Gu, W. Song, and T. C. Green, “Multi-agent reinforcement learning for active voltage control on power distribution networks,” Advances in Neural Information Processing Systems, vol. 34, pp. 3271–3284, 2021.
  11. D. Cao, J. Zhao, J. Hu, Y. Pei, Q. Huang, Z. Chen, and W. Hu, “Physics-informed graphical representation-enabled deep reinforcement learning for robust distribution system voltage control,” IEEE Transactions on Smart Grid, 2023.
  12. X. Zhang, Y. Liu, J. Duan, G. Qiu, T. Liu, and J. Liu, “DDPG-based multi-agent framework for SVC tuning in urban power grid with renewable energy resources,” IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5465–5475, 2021.
  13. H. Liu and W. Wu, “Online multi-agent reinforcement learning for decentralized inverter-based Volt-VAr control,” IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 2980–2990, 2021.
  14. A. Lang, Y. Wang, C. Feng, E. Stai, and G. Hug, “Data aggregation point placement for smart meters in the smart grid,” IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 541–554, 2021.
  15. B. Azimian, R. S. Biswas, A. Pal, and L. Tong, “Time synchronized state estimation for incompletely observed distribution systems using deep learning considering realistic measurement noise,” in 2021 IEEE Power & Energy Society General Meeting (PESGM), 2021, pp. 1–5.
  16. B. Azimian, R. S. Biswas, S. Moshtagh, A. Pal, L. Tong, and G. Dasarathy, “State and topology estimation for unobservable distribution systems using deep neural networks,” IEEE transactions on instrumentation and measurement, vol. 71, pp. 1–14, 2022.
  17. B. Azimian, S. Moshtagh, A. Pal, and S. Ma, “Analytical verification of deep neural network performance for time-synchronized distribution system state estimation,” Journal of Modern Power Systems and Clean Energy, p. to appear, 2023.
  18. B. Azimian, R. S. Biswas, and A. Pal, “Application of AI and machine learning algorithms in power system state estimation,” in Cyber-physical power systems: challenges and solutions by AI/ML, big data, blockchain, IoT, and information theory paradigms.   Wiley-IEEE Press, 2024.
  19. “IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces,” IEEE Std 1547-2018, pp. 1–138, 2018.
  20. T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
  21. Pecan Street Research Institute, “The Dataport Database.” [Online]. Available: https://pecanstreet.org/dataport/
Citations (1)

Summary

We haven't generated a summary for this paper yet.