Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning

Martin János Mayer, Bence Biró, Botond Szücs and Attila Aszódi

Applied Energy, 2023, vol. 336, issue C, No S0306261923001654

Abstract: The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles.

Keywords: Probabilistic simulation; Neural network; Hourly profile; Dunkelflaute; Security of supply; Carbon-free electricity generation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2023.120801

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