Summary:
Renewable energy sources (RES) are becoming increasingly prevalent in power systems, but their intermittent and unpredictable nature challenges deterministic optimal generation scheduling. Stochastic planning or operating methodologies offer superior performance compared to deterministic approaches, making renewable energy generation scenarios increasingly valuable inputs for multistage decision-making problems. In this paper, we introduce and compare three data-driven approaches for generating probabilistic renewable energy scenarios. Numerical results from both simulated and real-world datasets demonstrate the accuracy and computational efficiency of these methods. Our proposed approaches provide a powerful tool for creating precise and efficient probabilistic renewable energy scenarios, which can enhance optimal generation scheduling in power systems with high RES penetration.
Spanish layman's summary:
Este arículo explora tres métodos basados en datos para generar escenarios probabilísticos de energía renovable, abordando la variabilidad de las fuentes renovables. Probados en datos simulados y reales, estos enfoques mejoran la precisión y eficiencia en la programación de generación para sistemas eléctricos con alta penetración de energías renovables.
English layman's summary:
This paper explores three data-driven methods for generating probabilistic renewable energy scenarios, addressing the variability of renewable energy sources. Tested on simulated and real-world data, these approaches improve accuracy and efficiency in generation scheduling for power systems with high RES penetration.
Keywords: Bayesian linear regression; Gaussian processes; Probabilistic sampling; Probabilistic scenario generation; Solar-photovoltaic power; Wind power
JCR Impact Factor and WoS quartile: 4,000 - Q1 (2023)
DOI reference:
https://doi.org/10.1016/j.compeleceng.2024.109817
Published on paper: December 2024.
Published on-line: November 2024.
Citation:
C.D. Zuluaga-Ríos, C. Guarnizo-Lemus, Data-driven approaches for generating probabilistic short-term renewable energy scenarios. Computers and Electrical Engineering. Vol. 120, nº. Part C, pp. 109817-1 - 109817-18, December 2024. [Online: November 2024]
