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Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning (2211.09557v2)

Published 17 Nov 2022 in math.OC and stat.ML

Abstract: Distribution grids are challenged by rapid voltage fluctuations induced by variable power injections from distributed energy resources (DERs). To regulate voltage, the IEEE Standard 1547 recommends each DER inject reactive power according to piecewise-affine Volt/VAR control rules. Although the standard suggests a default shape, the rule can be customized per bus. This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles. ORD is formulated as a mixed-integer nonlinear program (MINLP), but scales unfavorably with the problem size. Towards a more efficient solution, we reformulate ORD as a deep learning problem. The idea is to design a DNN that emulates Volt/VAR dynamics. The DNN takes grid scenarios as inputs, rule parameters as weights, and outputs equilibrium voltages. Optimal rule parameters can be found by training the DNN so its output approaches unity for various scenarios. The DNN is only used to optimize rules and is never employed in the field. While dealing with ORD, we also review and expand on stability conditions and convergence rates for Volt/VAR dynamics on single- and multi-phase feeders. Tests showcase the merit of DNN-based ORD by benchmarking it against its MINLP counterpart.

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References (39)
  1. M. Farivar, R. Neal, C. Clarke, and S. Low, “Optimal inverter VAR control in distribution systems with high PV penetration,” in Proc. IEEE Power & Energy Society General Meeting, San Diego, CA, Jul. 2012.
  2. Y. Zhang, N. Gatsis, and G. B. Giannakis, “Robust energy management for microgrids with high-penetration renewables,” IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 944–953, Oct. 2013.
  3. F. Mancilla-David, A. Angulo, and A. Street, “Power management in active distribution systems penetrated by photovoltaic inverters: A data-driven robust approach,” IEEE Trans. Smart Grid, vol. 11, no. 3, pp. 2271–2280, May 2020.
  4. G. Cavraro, S. Bolognani, R. Carli, and S. Zampieri, “The value of communication in the voltage regulation problem,” in Proc. IEEE Conf. on Decision and Control, 2016, pp. 5781–5786.
  5. S. Magnússon, G. Qu, C. Fischione, and N. Li, “Voltage control using limited communication,” IEEE Trans. Control of Network Systems, vol. 6, no. 3, pp. 993–1003, Sep. 2019.
  6. M. Farivar, L. Chen, and S. Low, “Equilibrium and dynamics of local voltage control in distribution systems,” in Proc. IEEE Conf. on Decision and Control, Florence, Italy, Dec. 2013, pp. 4329–4334.
  7. X. Zhou, J. Tian, L. Chen, and E. Dall’Anese, “Local voltage control in distribution networks: A game-theoretic perspective,” in 2016 North American Power Symposium (NAPS), Nov 2016, pp. 1–6.
  8. X. Zhou, M. Farivar, Z. Liu, L. Chen, and S. H. Low, “Reverse and forward engineering of local voltage control in distribution networks,” IEEE Trans. Autom. Contr., vol. 66, no. 3, pp. 1116–1128, 2021.
  9. V. Calderaro, G. Conio, V. Galdi, G. Massa, and A. Piccolo, “Optimal decentralized voltage control for distribution systems with inverter-based distributed generators,” IEEE Trans. Power Syst., vol. 29, no. 1, pp. 230–241, Jan. 2014.
  10. A. Samadi, R. Eriksson, L. Söder, B. G. Rawn, and J. C. Boemer, “Coordinated active power-dependent voltage regulation in distribution grids with PV systems,” IEEE Trans. Power Del., vol. 29, no. 3, pp. 1454–1464, Jun 2014.
  11. A. Singhal, V. Ajjarapu, J. Fuller, and J. Hansen, “Real-time local Volt/Var control under external disturbances with high PV penetration,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 3849–3859, Jul. 2019.
  12. R. A. Jabr, “Segregated linear decision rules for inverter Watt-VAr control,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 2702–2708, 2021.
  13. K. Baker, A. Bernstein, E. Dall’Anese, and C. Zhao, “Network-cognizant voltage droop control for distribution grids,” IEEE Trans. Power Syst., vol. 33, no. 2, pp. 2098–2108, 2018.
  14. J. Sepulveda, A. Angulo, F. Mancilla-David, and A. Street, “Robust co-optimization of droop and affine policy parameters in active distribution systems with high penetration of photovoltaic generation,” IEEE Trans. Smart Grid, pp. 1–1, 2022.
  15. R. Xu, C. Zhang, Y. Xu, Z. Dong, and R. Zhang, “Multi-objective hierarchically-coordinated volt/var control for active distribution networks with droop-controlled pv inverters,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 998–1011, Mar 2022.
  16. T. O. Olowu, A. Inaolaji, A. Sarwat, and S. Paudyal, “Optimal Volt-VAR and Volt-Watt droop settings of smart inverters,” in IEEE Green Technologies Conference, 2021, pp. 89–96.
  17. A. Inaolaji, A. Savasci, and S. Paudyal, “Optimal droop settings of smart inverters,” in IEEE Photovoltaic Specialists Conference, 2021, pp. 2584–2589.
  18. A. Savasci, A. Inaolaji, and S. Paudyal, “Distribution grid optimal power flow with adaptive Volt-VAr droop of smart inverters,” in IEEE Industry Applications Society Annual Meeting, Vancouver, BC, 2021, pp. 1–8.
  19. A. Inaolaji, A. Savasci, and S. Paudyal, “Distribution grid optimal power flow in unbalanced multiphase networks with Volt-VAr and Volt-Watt droop settings of smart inverters,” IEEE Trans. Ind. Applicat., vol. 58, no. 5, pp. 5832–5843, 2022.
  20. 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 Trans. Sustain. Energy, vol. 13, no. 2, pp. 868–881, Apr. 2022.
  21. I. Murzakhanov, S. Gupta, S. Chatzivasileiadis, and V. Kekatos, “Optimal design of Volt/VAR control rules for inverter-interfaced distributed energy resources,” IEEE Trans. Smart Grid, 2023, (to appear).
  22. W. Wang, N. Yu, Y. Gao, and J. Shi, “Safe off-policy deep reinforcement learning algorithm for Volt-VAR control in power distribution systems,” IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3008–3018, Jul. 2020.
  23. Q. Zhang, K. Dehghanpour, Z. Wang, F. Qiu, and D. Zhao, “Multi-agent safe policy learning for power management of networked microgrids,” IEEE Trans. Smart Grid, vol. 12, no. 2, pp. 1048–1062, Mar. 2021.
  24. S. Gupta, V. Kekatos, and M. Jin, “Controlling smart inverters using proxies: A chance-constrained DNN-based approach,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1310–1321, Mar. 2022.
  25. M. K. Singh, S. Gupta, V. Kekatos, G. Cavraro, and A. Bernstein, “Learning to optimize power distribution grids using sensitivity-informed deep neural networks,” in Proc. IEEE Intl. Conf. on Smart Grid Commun., Tempe, AZ, Nov. 2020, pp. 1–6.
  26. M. Jalali, V. Kekatos, N. Gatsis, and D. Deka, “Designing reactive power control rules for smart inverters using support vector machines,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1759–1770, Mar. 2020.
  27. M. Jalali, M. K. Singh, V. Kekatos, G. B. Giannakis, and C. C. Liu, “Fast inverter control by learning the OPF mapping using sensitivity-informed Gaussian processes,” IEEE Trans. Smart Grid, vol. 14, no. 3, pp. 2432–2445, May 2022.
  28. W. Cui, J. Li, and B. Zhang, “Decentralized safe reinforcement learning for inverter-based voltage control,” Electric Power Systems Research, vol. 211, p. 108609, 2022.
  29. W. Cui, Y. Jiang, and B. Zhang, “Reinforcement learning for optimal primary frequency control: A Lyapunov approach,” IEEE Trans. Power Syst., vol. 38, no. 2, pp. 1676–1688, Mar 2023.
  30. Y. Shi, G. Qu, S. Low, A. Anandkumar, and A. Wierman, “Stability constrained reinforcement learning for real-time voltage control,” in Proc. American Control Conf., Atlanta, GA, Jun. 2022, pp. 2715–2721.
  31. G. Cavraro, Z. Yuan, M. K. Singh, and J. Cortes, “Learning local Volt/Var controllers towards efficient network operation with stability guarantees,” in Proc. IEEE Conf. on Decision and Control, Cancun, Mexico, Dec. 2022, pp. 5056–5061.
  32. S. Taheri, M. Jalali, V. Kekatos, and L. Tong, “Fast probabilistic hosting capacity analysis for active distribution systems,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2000–2012, May 2021.
  33. M. Baran and F. Wu, “Optimal sizing of capacitors placed on a radial distribution system,” IEEE Trans. Power Syst., vol. 4, no. 1, pp. 735–743, Jan. 1989.
  34. V. Kekatos, L. Zhang, G. B. Giannakis, and R. Baldick, “Voltage regulation algorithms for multiphase power distribution grids,” IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3913–3923, Sep. 2016.
  35. P. Jahangiri and D. C. Aliprantis, “Distributed Volt/VAr control by PV inverters,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3429–3439, Aug. 2013.
  36. J. Wei, S. Gupta, D. C. Aliprantis, and V. Kekatos, “A chance-constrained optimal design of Volt/VAR control rules for distributed energy resources,” in Proc. North American Power Symposium, Ashville, NC, Oct. 2023. [Online]. Available: https://arxiv.org/abs/2306.06467
  37. M. K. Singh, S. Taheri, V. Kekatos, K. P. Schneider, and C.-C. Liu, “Joint grid topology reconfiguration and design of Watt-VAR curves for DERs,” in Proc. IEEE Power & Energy Society General Meeting, Denver, CO, Jul. 2022.
  38. D. Chen, S. Iyengar, D. Irwin, and P. Shenoy, “Sunspot: Exposing the location of anonymous solar-powered homes,” in ACM Intl. Conf. on Systems for Energy-Efficient Built Environ., Palo Alto, CA, Nov. 2016.
  39. J. Lofberg, “A toolbox for modeling and optimization in MATLAB,” in Proc. of the CACSD Conf., 2004.
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