Real-time frequency estimation in power systems using ℓ1-regularized kriging
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12250Keywords:
Learning for control, Nonparametric methods, Smart grids, Constraint and security monitoring and control, Control of renewable energy resources, Data-based controlAbstract
We address the real-time forecast of frequency deviations in the grid. We take a data-driven approach and rely on ordinary kriging interpolation. We present a nonparametric prediction oracle which predicts the grid frequency from past frequency trajectories and current injections. We showcase our approach on a simulated distribution network node.
References
Álamo, T., Krupa, P., & Limón, D. (2019). Gradient based restart FISTA. En Proceedings of the 58th IEEE Conference on Decision and Control (CDC) (pp. 3936–3941). IEEE.
Beck, A. (2017). First‐order Methods in Optimization. SIAM.
CENELEC (European Committee for Electrotechnical Standardisation). (2011). Voltage characteristics of electricity supplied by public distribution systems. European Norm EN 50160.
Cressie, N. (1990). The origins of Kriging. Mathematical Geology, 22(3), 239–252.
Cressie, N. (1993). Statistics for Spatial Data. John Wiley & Sons.
Denninger, R., Probst, L., Burger, B., Schultz, A., & Dressler, Y. (2021). Visualisierung des Potentials netzbildender Wechselrichter zur Bereitstellung von Reserveleistung. Recuperado de [https://www.energy-charts.info/charts/frequency/chart.htm?l=en&c=DE×lider=1&hour=11&datetimepicker=28.04.2025]
Diario de Sevilla. (2025). Pérdidas de generación precedieron apagón Granada–Badajoz–Sevilla. Diario de Sevilla. Recuperado de [https://www.diariodesevilla.es/economia/perdidas-generacion-precedieron-apagon-granada-badajoz-sevilla_0_2003930695.html]
Domínguez, X., Prado, A., Arboleya, P., & Terzija, V. (2023). Evolution of knowledge mining from data in power systems: The big data analytics breakthrough. *Electric Power Systems Research, 218*, Art. 109193.
Ekomwenrenren, E., Simpson‐Porco, J. W., Farantatos, E., Patel, M., Haddadi, A., & Zhu, L. (2023). Data‐driven fast frequency control using inverter‐based resources. IEEE Transactions on Power Systems.
ENTSO‐E. (2025). ENTSO‐E expert panel initiates the investigation into the causes of Iberian blackout. Recuperado de [https://www.entsoe.eu/news/2025/05/09/entso-e-expert-panel-initiates-the-investigation-into-the-causes-of-iberian-blackout/](https://www.entsoe.eu/news/2025/05/09/entso-e-expert-panel-initiates-the-investigation-into-the-causes-of-iberian-blackout/)
IEEE Power & Energy Society. (2021). IEEE Draft Standard for Harmonic Control in Electric Power Systems (IEEE P519/D5.1) (pp. 1–30). IEEE.
Kruse, J., Schäfer, B., & Witthaut, D. (2020). Predictability of power grid frequency. IEEE Access, 8, 149 435–149 446.
Moreno‐Blázquez, C., Fele, F., Limón, D., & Álamo, T. (2024). Predicción de voltajes en la red eléctrica por interpolación kriging. Revista Iberoamericana de Automática e Informática Industrial.
North American Electric Reliability Corporation (NERC). (2020). Fast frequency response concepts and bulk power system reliability needs. Recuperado de [https://www.nerc.com/comm/PC/Pages/Inverter-Based-Resource-Performance-Task-Force.aspx](https://www.nerc.com/comm/PC/Pages/Inverter-Based-Resource-Performance-Task-Force.aspx)
Persis, C. D., & Tesi, P. (2019). On persistency of excitation and formulas for data‐driven control. En Proceedings of the 58th IEEE Conference on Decision and Control (CDC) (pp. 873–878). IEEE.
Tibshirani, R., & Wasserman, L. (2017). Sparsity, the Lasso, and Friends [Notas de clase en “Statistical Machine Learning”]. Carnegie Mellon University, Spring 2017.
Wang, W., Yao, W., Chen, C., Deng, X., & Liu, Y. (2020). Fast and accurate frequency response estimation for large power system disturbances using second derivative of frequency data. IEEE Transactions on Power Systems, 35(3), 2483–2486.
Wikipedia contributors. (2025). 2025 Iberian Peninsula blackout. Wikipedia, The Free Encyclopedia. Recuperado el 30 de mayo de 2025, de [https://en.wikipedia.org/wiki/2025_Iberian_Peninsula_blackout](https://en.wikipedia.org/wiki/2025_Iberian_Peninsula_blackout)
Xu, T., & Overbye, T. (2015). Real‐time event detection and feature extraction using PMU measurement data. En Proceedings of the 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 265–270). IEEE.
Zhang, Q., & Ljung, L. (2004). Multiple steps prediction with nonlinear ARX models. IFAC Proceedings Volumes, 37(13), 309–314. En 6th IFAC Symposium on Nonlinear Control Systems 2004 (NOLCOS 2004), Stuttgart, Germany, 1–3 September 2004.
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Copyright (c) 2025 Carlos Moreno-Blázquez, Filiberto Fele, Teodoro Alamo
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