«Scientific articles in collaboration with research institutes and universities»
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Annals of Operations Research
Abstract
In this study, we introduce a conservative model designed to accurately predict renewable energy generation while addressing fluctuations and imbalances in power supply. We employ a time-inhomogeneous Vasicek model driven by both a skew Brownian motion and a compound Poisson process, providing proof of solution existence, uniqueness, and mean reversion. Our focus is on minimizing deficits caused by underestimating actual generation levels. By prioritizing a positively skewed distribution of deviations and aiming for them to cluster around zero, we achieve significant reductions in forecasting errors compared to established benchmarks. Utilizing hourly data from Terna in 2023, our model illustrates the effects of energy imbalances, which lead to financial losses, yet also highlights enhanced cost savings and significant reductions in CO2 emissions.
Keywords: Skew Brownian motion, Electricity, Forecasting, Vasicek model, Renewable Energy, CO2
JEL Classification: 60j65, 91B70, 91B84, 91B76
Cite: Ascione, G., Bufalo, M., Orlando, G., & Quadrini, R. (2024). Balancing the grid: mitigating the effects of renewable energy in Italy via skew modeling and forecasting. Annals of Operations Research, OnlineFirst, 1-39.
Publisher: Springer Journals
Copyright: Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN: 0254-5330
eISSN: 1572-9338