Papers
Topics
Authors
Recent
Search
2000 character limit reached

Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning

Published 20 Sep 2023 in stat.ML, cs.CE, and cs.LG | (2309.11067v1)

Abstract: We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserves shortfall and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (22)
  1. M. Ludkovski, G. Swindle, and E. Grannan, “Large scale probabilistic simulation of renewables production,” arXiv preprint arXiv:2205.04736, 2022.
  2. X. Zhu, Z. Yu, and X. Liu, “Security constrained unit commitment with extreme wind scenarios,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 3, pp. 464–472, 2020.
  3. I. Satkauskas, J. Maack, M. Reynolds, D. Sigler, K. Panda, and W. Jones, “Simulating impacts of extreme events on grids with high penetrations of wind power resources,” in 2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).   IEEE, 2022, pp. 01–05.
  4. A. Cuevas, M. Febrero, and R. Fraiman, “Robust estimation and classification for functional data via projection-based depth notions,” Computational Statistics, vol. 22, no. 3, pp. 481–496, 2007.
  5. M. Hubert, P. Rousseeuw, and P. Segaert, “Multivariate and functional classification using depth and distance,” Advances in Data Analysis and Classification, vol. 11, pp. 445–466, 2017.
  6. C. Sguera, P. Galeano, and R. Lillo, “Spatial depth-based classification for functional data,” Test, vol. 23, pp. 725–750, 2014.
  7. X. Zhang, Y. Tian, G. Guan, and Y. R. Gel, “Depth-based classification for relational data with multiple attributes,” Journal of Multivariate Analysis, vol. 184, p. 104732, 2021.
  8. K. Luo, W. Shi, and W. Wang, “Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method,” Global Energy Interconnection, vol. 3, no. 2, pp. 140–148, 2020.
  9. B. Farjad, A. Gupta, H. Sartipizadeh, and A. Cannon, “A novel approach for selecting extreme climate change scenarios for climate change impact studies,” Science of the Total Environment, vol. 678, pp. 476–485, 2019.
  10. J.-L. Wang, J.-M. Chiou, and H.-G. Müller, “Functional data analysis,” Annual Review of Statistics and Its Application, vol. 3, pp. 257–295, 2016.
  11. R. Fraiman and G. Muniz, “Trimmed means for functional data,” Test, vol. 10, no. 2, pp. 419–440, 2001.
  12. N. N. Narisetty and V. N. Nair, “Extremal depth for functional data and applications,” Journal of the American Statistical Association, vol. 111, no. 516, pp. 1705–1714, 2016.
  13. Y. Zuo and R. Serfling, “General notions of statistical depth function,” Annals of Statistics, pp. 461–482, 2000.
  14. W. Dai, T. Mrkvička, Y. Sun, and M. G. Genton, “Functional outlier detection and taxonomy by sequential transformations,” Computational Statistics & Data Analysis, vol. 149, p. 106960, 2020.
  15. J. A. Cuesta-Albertos and A. Nieto-Reyes, “The random Tukey depth,” Computational Statistics & Data Analysis, vol. 52, no. 11, pp. 4979–4988, 2008.
  16. A. B. Birchfield, T. J. Overbye, and K. R. Davis, “Educational applications of large synthetic power grids,” IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 765–772, 2018.
  17. M. Grzadkowski and H. Manning, “ohsu-comp-bio/dryads-research: BMC Bioinformatics Resubmission w/ DOI,” Apr. 2021.
  18. B. Knueven, C. Laird, J.-P. Watson, M. Bynum, A. Castillo, and USDOE, “Egret v. 0.1 (beta), version v. 0.1 (beta),” 3 2019.
  19. B. Knueven, J. Ostrowski, and J.-P. Watson, “On mixed-integer programming formulations for the unit commitment problem,” INFORMS Journal on Computing, vol. 32, no. 4, pp. 857–876, 2020.
  20. Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023.
  21. O. Ojo, R. E. Lillo, and A. F. Anta, “Outlier detection for functional data with R package fdaoutlier,” arXiv preprint arXiv:2105.05213, 2021.
  22. M. Febrero-Bande and M. O. de la Fuente, “Statistical computing in functional data analysis: The R package fda.usc,” Journal of statistical Software, vol. 51, pp. 1–28, 2012.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up