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Weather-driven electricity systems

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Metadaten
Document Type:Doctoral Thesis
Language:English
Author(s):Raffaele Sgarlato
Advisor:Lion Hirth, David J. Brayshaw, Lynn Kaack
Hertie Collections (Serial Number):Dissertations submitted to the Hertie School (09/2023)
Publication year:2023
Publishing Institution:Hertie School
Granting Institution:Hertie School
Thesis date:2023/10/10
Release Date:2023/11/16
Notes:
List of publications:

Sgarlato, R., & Ziel, F. (2023). The role of weather predictions in electricity price forecasting beyond the day-ahead horizon. IEEE Transactions on Power Systems, 38(3), 2500–2511. https://doi.org/10.1109/TPWRS.2022.3180119

Sgarlato, R. (2023). Statistical electricity price forecasting: A structural approach. https://doi.org/10.48550/ARXIV.2306.14186

Tiedemann, S., Sgarlato, R., & Hirth, L. (2023). Price elasticity of electricity demand: Using instrumental variable regressions to address endogeneity and autocorrelation of high-frequency time series. https://doi.org/10.48550/ARXIV.2306.12863

Ruhnau, O., Eicke, A., Sgarlato, R., Tröndle, T., & Hirth, L. (2022). Cost-potential curves of onshore wind energy: The role of disamenity costs. Environmental and Resource Economics. https://doi.org/10.1007/s10640-022-00746-2
Notes:
Shelf mark: 2023D009 + 2023D009+1
Hertie School Research:Publications PhD Researchers
Licence of document (German):Metadaten / metadata
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