Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 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

Long Solution Times or Low Solution Quality: On Trade-Offs in Choosing a Power Flow Formulation for the Optimal Power Shutoff Problem (2310.13843v2)

Published 20 Oct 2023 in math.OC, cs.SY, and eess.SY

Abstract: The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in the related contexts of optimal transmission switching or grid restoration planning. This paper explores the accuracy of the DC OPS formulation and the ability to recover an AC-feasible power flow solution after de-energization decisions are made. We also extend the OPS problem to include variants with the AC, Second-Order-Cone, and Network-Flow power flow equations, and compare them to the DC approximation with respect to solution quality and time. The results highlight that the DC approximation overestimates the amount of load that can be served, leading to poor de-energization decisions. The AC and SOC-based formulations are better, but prohibitively slow to solve for even modestly sized networks thus demonstrating the need for new solution methods with better trade-offs between computational time and solution quality.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. H.-O. Pörtner, D. C. Roberts, E. S. Poloczanska et al., “Ipcc, 2022: Summary for policymakers,” 2022.
  2. “Pacific gas and electric company public safety power shutoff (psps) report to the cpuc october 23-25, 2019 de-energization event,” Pacific Gas & Electric. [Online]. Available: https://tinyurl.com/2s3uzxau
  3. N. Rhodes, L. Ntaimo, and L. Roald, “Balancing wildfire risk and power outages through optimized power shut-offs,” IEEE Trans. on Power Syst., vol. 36, no. 4, pp. 3118–3128, 2020.
  4. A. Kody, R. Piansky, and D. K. Molzahn, “Optimizing transmission infrastructure investments to support line de-energization for mitigating wildfire ignition risk,” arXiv preprint arXiv:2203.10176, 2022.
  5. A. Astudillo, B. Cui, and A. S. Zamzam, “Managing power systems-induced wildfire risks using optimal scheduled shutoffs,” National Renewable Energy Lab, Golden, CO (United States), Tech. Rep., 2022.
  6. W. Yang, S. N. Sparrow, M. Ashtine et al., “Resilient by design: Preventing wildfires and blackouts with microgrids,” Applied Energy, vol. 313, p. 118793, 2022.
  7. R. Hanna, “Optimal investment in microgrids to mitigate power outages from public safety power shutoffs,” in 2021 IEEE Power & Energy Society General Meeting (PESGM).   IEEE, 2021, pp. 1–5.
  8. S. Taylor and L. A. Roald, “A framework for risk assessment and optimal line upgrade selection to mitigate wildfire risk,” 2022 Power Syst. Comput. Conf.
  9. A. Z. Bertoletti and J. C. do Prado, “Transmission system expansion planning under wildfire risk,” in 2022 North Am. Power Symp.
  10. R. Bayani and S. D. Manshadi, “Resilient expansion planning of electricity grid under prolonged wildfire risk,” IEEE Trans. on Smart Grid, 2023.
  11. K. Baker, “Solutions of dc opf are never ac feasible,” in Proc. of the Twelfth ACM Int. Conf. on Future Energy Syst., 2021, pp. 264–268.
  12. W. Hong, B. Wang, M. Yao et al., “Data-driven power system optimal decision making strategy underwildfire events,” Lawrence Livermore National Lab, Tech. Rep., 2022.
  13. K. W. Hedman, S. S. Oren, and R. P. O’Neill, “A review of transmission switching and network topology optimization,” in 2011 IEEE power and energy society general meeting.   IEEE, 2011, pp. 1–7.
  14. M. Marwali and S. Shahidehpour, “Integrated generation and transmission maintenance scheduling with network constraints,” in Proc. of the 20th Int. Conf. on Power Ind. Comput. Appl.   IEEE, 1997, pp. 37–43.
  15. S. Lumbreras and A. Ramos, “The new challenges to transmission expansion planning. survey of recent practice and literature review,” Electr. Power Syst. Res., vol. 134, pp. 19–29, 2016.
  16. C. Barrows, S. Blumsack, and P. Hines, “Correcting optimal transmission switching for ac power flows,” in 2014 47th Hawaii Int. Conf. Syst Sci.   IEEE, 2014, pp. 2374–2379.
  17. Y. Bai, H. Zhong, Q. Xia, and C. Kang, “A two-level approach to ac optimal transmission switching with an accelerating technique,” IEEE Trans. on Power Syst., vol. 32, no. 2, pp. 1616–1625, 2016.
  18. C. Crozier, K. Baker, and B. Toomey, “Feasible region-based heuristics for optimal transmission switching,” Sustainable Energy, Grids and Networks, vol. 30, p. 100628, 2022.
  19. E. S. Johnson, S. Ahmed, S. S. Dey, and J.-P. Watson, “A k-nearest neighbor heuristic for real-time dc optimal transmission switching,” arXiv preprint arXiv:2003.10565, 2020.
  20. F. Capitanescu and L. Wehenkel, “An ac opf-based heuristic algorithm for optimal transmission switching,” in 2014 Power Syst. Comput. Conf.
  21. C. Coffrin, R. Bent, B. Tasseff et al., “Relaxations of ac maximal load delivery for severe contingency analysis,” IEEE Trans. on Power Syst., vol. 34, no. 2, pp. 1450–1458, March 2019.
  22. N. Rhodes, D. M. Fobes, C. Coffrin, and L. Roald, “Powermodelsrestoration. jl: An open-source framework for exploring power network restoration algorithms,” in 2021 Power Syst. Comput. Conf.
  23. N. Rhodes and L. Roald, “The role of distributed energy resources in distribution system restoration,” 2022 Hawaii Int. Conf. Syst Sci., 2022.
  24. N. Rhodes, C. Coffrin, and L. Roald, “Security constrained optimal power shutoff,” arXiv preprint arXiv:2304.13778, 2023.
  25. C. Coffrin, H. L. Hijazi, and P. Van Hentenryck, “The qc relaxation: A theoretical and computational study on optimal power flow,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3008–3018, 2015.
  26. R. A. Jabr, “Radial distribution load flow using conic programming,” IEEE Trans. on Power Syst., vol. 21, no. 3, pp. 1458–1459, 2006.
  27. C. Coffrin, H. Hijazi, and P. Van Hentenryck, “Network flow and copper plate relaxations for ac transmission systems,” in 2016 Power Systems Computation Conference (PSCC).   IEEE, 2016, pp. 1–8.
  28. J. Bezanson, A. Edelman, S. Karpinski, and V. Shah, “Julia: A fresh approach to numerical computing,” SIAM Rev., vol. 59, no. 1, pp. 65–98, 2017. [Online]. Available: https://doi.org/10.1137/141000671
  29. I. Dunning, J. Huchette, and M. Lubin, “Jump: A modeling language for mathematical optimization,” SIAM Rev., vol. 59, pp. 295–320, 2017.
  30. Gurobi Optimization, Inc., “Gurobi optimizer reference manual,” Published online at http://www.gurobi.com, 2014.
  31. O. Kröger, C. Coffrin, H. Hijazi, and H. Nagarajan, “Juniper: An open-source nonlinear branch-and-bound solver in julia,” in Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 15th Int. Conf., Delft, The Netherlands, June 26–29, 2018, Proc. 15.   Springer, 2018, pp. 377–386.
  32. A. Wächter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Mathematical programming, vol. 106, pp. 25–57, 2006.
  33. D. Lin, J. M. White, S. Byrne et al., “JuliaStats/Distributions.jl: a Julia package for probability distributions and associated functions,” Jul. 2019. [Online]. Available: https://doi.org/10.5281/zenodo.2647458
  34. N. Rhodes, “PowerPlots.jl,” 2023. [Online]. Available: https://github.com/WISPO-POP/PowerPlots.jl
  35. S. Christ, D. Schwabeneder, C. Rackauckas et al., “Plots.jl – a user extendable plotting api for the julia programming language,” J. of Open Research Software, 2023.
  36. PGLib Optimal Power Flow Benchmarks, “The ieee pes task force on benchmarks for validation of emerging power system algorithms,” Published online at https://github.com/power-grid-lib/pglib-opf, 2021.
  37. S. Pishgar-Komleh, A. Keyhani, and P. Sefeedpari, “Wind speed and power density analysis based on weibull and rayleigh distributions (a case study: Firouzkooh county of iran),” Renewable and sustainable energy reviews, vol. 42, pp. 313–322, 2015.
  38. O. Rios, W. Jahn, E. Pastor et al., “Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications,” Int. J. of Wildland Fire, vol. 27, pp. 257–270, 2018.
Citations (4)

Summary

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