Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 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

Optimal Power Flow Pursuit via Feedback-based Safe Gradient Flow (2312.12267v3)

Published 19 Dec 2023 in eess.SY, cs.SY, and math.OC

Abstract: This paper considers the problem of controlling inverter-interfaced distributed energy resources (DERs) in a distribution grid to solve an AC optimal power flow (OPF) problem in real time. The AC OPF includes voltage constraints, and seeks to minimize costs associated with the economic operation, power losses, or the power curtailment from renewables. We develop an online feedback optimization method to drive the DERs' power setpoints to solutions of an AC OPF problem based only on voltage measurements (and without requiring measurements of the power consumption of non-controllable assets). The proposed method - grounded on the theory of control barrier functions - is based on a continuous approximation of the projected gradient flow, appropriately modified to accommodate measurements from the power network. We provide results in terms of local exponential stability, and assess the robustness to errors in the measurements and in the system Jacobian matrix. We show that the proposed method ensures anytime satisfaction of the voltage constraints when no model and measurement errors are present; if these errors are present and are small, the voltage violation is practically negligible. We also discuss extensions of the framework to virtual power plant setups and to cases where constraints on power flows and currents must be enforced. Numerical experiments on a 93-bus distribution system and with realistic load and production profiles show a superior performance in terms of voltage regulation relative to existing methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. J. A. Taylor, S. V. Dhople, and D. S. Callaway, “Power systems without fuel,” Renewable and Sustainable Energy Reviews, vol. 57, pp. 1322–1336, 2016.
  2. B. Kroposki, A. Bernstein, J. King, D. Vaidhynathan, X. Zhou, C.-Y. Chang, and E. Dall’Anese, “Autonomous energy grids: Controlling the future grid with large amounts of distributed energy resources,” IEEE Power and Energy Magazine, vol. 18, no. 6, pp. 37–46, 2020.
  3. Y. Zhu and K. Tomsovic, “Optimal distribution power flow for systems with distributed energy resources,” International Journal of Electrical Power & Energy Systems, vol. 29, no. 3, pp. 260–267, 2007.
  4. F. Capitanescu, “Critical review of recent advances and further developments needed in AC optimal power flow,” Electric Power Systems Research, vol. 136, pp. 57–68, 2016.
  5. K. Baker, “Emulating AC OPF solvers for obtaining sub-second feasible, near-optimal solutions,” arXiv preprint arXiv:2012.10031, 2020.
  6. R. Nellikkath and S. Chatzivasileiadis, “Physics-informed neural networks for AC optimal power flow,” Electric Power Systems Research, vol. 212, p. 108412, 2022.
  7. S. Bolognani, R. Carli, G. Cavraro, and S. Zampieri, “Distributed reactive power feedback control for voltage regulation and loss minimization,” IEEE Transactions on Automatic Control, vol. 60, no. 4, pp. 966–981, 2014.
  8. E. Dall’Anese and A. Simonetto, “Optimal power flow pursuit,” IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 942–952, 2016.
  9. A. Hauswirth, S. Bolognani, G. Hug, and F. Dorfler, “Projected gradient descent on Riemannian manifolds with applications to online power system optimization,” in 54th Annual Allerton Conference on Communication, Control, and Computing, Sept 2016, pp. 225–232.
  10. A. Hauswirth, A. Zanardi, S. Bolognani, G. Hug, and F. Dorfler, “Online optimization in closed loop on the power flow manifold,” in IEEE PES PowerTech conference, 2017.
  11. S. Nowak, Y. C. Chen, and L. Wang, “A measurement-based gradient-descent method to optimally dispatch DER reactive power,” in IEEE Photovoltaic Specialists Conference, 2020.
  12. L. Ortmann, C. Rubin, A. Scozzafava, J. Lehmann, S. Bolognani, and F. Dörfler, “Deployment of an online feedback optimization controller for reactive power flow optimization in a distribution grid,” arXiv preprint arXiv:2305.06702, 2023.
  13. Z. Yuan, G. Cavraro, M. K. Singh, and J. Cortés, “Learning provably stable local Volt/Var controllers for efficient network operation,” IEEE Transactions on Power Systems, 2023.
  14. A. Bernstein and E. Dall’Anese, “Real-time feedback-based optimization of distribution grids: A unified approach,” IEEE Transactions on Control of Network Systems, vol. 6, no. 3, pp. 1197–1209, 2019.
  15. Y. Chen, A. Bernstein, A. Devraj, and S. Meyn, “Model-free primal-dual methods for network optimization with application to real-time optimal power flow,” in American Control Conference, 2020, pp. 3140–3147.
  16. J. C. Olives-Camps, Á. R. del Nozal, J. M. Mauricio, and J. M. Maza-Ortega, “A model-less control algorithm of DC microgrids based on feedback optimization,” International Journal of Electrical Power & Energy Systems, vol. 141, p. 108087, 2022.
  17. L. Gan and S. H. Low, “An online gradient algorithm for optimal power flow on radial networks,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 625–638, 2016.
  18. A. Bernstein, L. Reyes-Chamorro, J.-Y. Le Boudec, and M. Paolone, “A composable method for real-time control of active distribution networks with explicit power setpoints. Part I: Framework,” Electric Power Systems Research, vol. 125, pp. 254–264, 2015.
  19. Y. Tang, K. Dvijotham, and S. Low, “Real-time optimal power flow,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2963–2973, 2017.
  20. J.-L. Lupien, I. Shames, and A. Lesage-Landry, “Online interior-point methods for time-varying equality-constrained optimization,” arXiv preprint arXiv:2307.16128, 2023.
  21. A. Colot, T. Stegen, and B. Cornélusse, “Fully distributed real-time voltage control in active distribution networks with large penetration of solar inverters,” in IEEE Belgrade PowerTech, 2023.
  22. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in European control conference, 2019, pp. 3420–3431.
  23. A. Allibhoy and J. Cortés, “Control barrier function-based design of gradient flows for constrained nonlinear programming,” IEEE Transactions on Automatic Control, vol. 69, no. 6, 2024.
  24. D. Sarajlić and C. Rehtanz, “Low voltage benchmark distribution network models based on publicly available data,” in IEEE PES Innovative Smart Grid Technologies Europe, 2019.
  25. S. Bolognani and S. Zampieri, “On the existence and linear approximation of the power flow solution in power distribution networks,” IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 163–172, 2015.
  26. A. Bernstein, C. Wang, E. Dall’Anese, J.-Y. Le Boudec, and C. Zhao, “Load flow in multiphase distribution networks: Existence, uniqueness, non-singularity and linear models,” IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 5832–5843, 2018.
  27. C. Wang, A. Bernstein, J.-Y. Le Boudec, and M. Paolone, “Existence and uniqueness of load-flow solutions in three-phase distribution networks,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3319–3320, 2017.
  28. A. V. Fiacco, “Sensitivity analysis for nonlinear programming using penalty methods,” Mathematical programming, vol. 10, no. 1, pp. 287–311, 1976.
  29. A. Hauswirth, S. Bolognani, G. Hug, and F. Dörfler, “Generic existence of unique lagrange multipliers in ac optimal power flow,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 791–796, 2018.
  30. G. Banjac, B. Stellato, N. Moehle, P. Goulart, A. Bemporad, and S. Boyd, “Embedded code generation using the OSQP solver,” in IEEE Conference on Decision and Control, 2017, pp. 1906–1911.
  31. S. V. Dhople, S. S. Guggilam, and Y. C. Chen, “Linear approximations to AC power flow in rectangular coordinates,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 211–217.
  32. V. Kekatos, L. Zhang, G. B. Giannakis, and R. Baldick, “Voltage regulation algorithms for multiphase power distribution grids,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3913–3923, 2015.
  33. A. Angioni, T. Schlösser, F. Ponci, and A. Monti, “Impact of pseudo-measurements from new power profiles on state estimation in low-voltage grids,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 1, pp. 70–77, 2015.
  34. J. Liu, “Sensitivity analysis in nonlinear programs and variational inequalities via continuous selections,” SIAM Journal on Control and Optimization, vol. 33, no. 4, pp. 1040–1060, 1995.
  35. H. K. Khalil, “Nonlinear systems,” Patience Hall, 2002.
  36. A. Eggli, S. Karagiannopoulos, S. Bolognani, and G. Hug, “Stability analysis and design of local control schemes in active distribution grids,” IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 1900–1909, 2020.
  37. M. Baran and F. Wu, “Optimal capacitor placement on radial distribution systems,” IEEE Transactions on Power Delivery, vol. 4, no. 1, pp. 725–734, 1989.
  38. “Requirements for generating plants to be connected in parallel with distribution networks – Part 2: connection to a MV distribution network – generating plants up to and including type B,” CENELEC, pp. 1–84, 2019.
  39. M. Nagumo, “Über die lage der integralkurven gewöhnlicher differentialgleichungen,” Proceedings of the Physico-Mathematical Society of Japan. 3rd Series, vol. 24, pp. 551–559, 1942.
Citations (2)

Summary

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