TY - JOUR A1 - Sgarlato, Raffaele A1 - Ziel, Florian T1 - The Role of Weather Predictions in Electricity Price Forecasting Beyond the Day-Ahead Horizon JF - IEEE Transactions on Power Systems N2 - Forecasts of meteorology-driven factors, such as intermittent renewable generation, are commonly included in electricity price forecasting models. We show that meteorological forecasts can be used directly to improve price forecasts multiple days in advance. We introduce an autoregressive multivariate linear model with exogenous variables and LASSO for variable selection and regularization. We used variants of this model to forecast German wholesale prices up to ten days in advance and evaluate the benefit of adding meteorological forecasts, namely wind speed and direction, solar irradiation, cloud cover, and temperature forecasts of selected locations across Europe. The resulting regression coefficients are analyzed with regard to their spatial as well as temporal distribution and are put in context with underlying power market fundamentals. Wind speed in northern Germany emerges as a particularly strong explanatory variable. The benefit of adding meteorological forecasts strongest when autoregressive effects are weak, yet the accuracy of the meteorological forecasts is sufficient for the model to identify patterns. Forecasts produced 2-4 days in advance exhibit an improvement in RMSE by 10-20%. Furthermore, the forecasting horizon is shown to impact the choice of the regularization penalty that tends to increase at longer forecasting horizons. KW - Centre for Sustainability Y1 - 2022 U6 - https://doi.org/10.1109/TPWRS.2022.3180119 ER - TY - THES A1 - Sgarlato, Raffaele T1 - Weather-driven electricity systems T3 - Dissertations submitted to the Hertie School - 09/2023 Y1 - 2023 N1 - 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 N1 - Shelf mark: 2023D009 + 2023D009+1 ER - TY - JOUR A1 - Ruhnau, Oliver A1 - Eicke, Anselm A1 - Sgarlato, Raffaele A1 - Tröndle, Tim A1 - Hirth, Lion T1 - Cost-Potential Curves of Onshore Wind Energy: the Role of Disamenity Costs JF - Environmental and Resource Economics N2 - Numerical optimization models are used to develop scenarios of the future energy system. Usually, they optimize the energy mix subject to engineering costs such as equipment and fuel. For onshore wind energy, some of these models use cost-potential curves that indicate how much electricity can be generated at what cost. These curves are upward sloping mainly because windy sites are occupied first and further expanding wind energy means deploying less favorable resources. Meanwhile, real-world wind energy expansion is curbed by local resistance, regulatory constraints, and legal challenges. This presumably reflects the perceived adverse effect that onshore wind energy has on the local human population, as well as other negative external effects. These disamenity costs are at the core of this paper. We provide a comprehensive and consistent set of cost-potential curves of wind energy for all European countries that include disamenity costs, and which can be used in energy system modeling. We combine existing valuation of disamenity costs from the literature that describe the costs as a function of the distance between turbine and households with gridded population data, granular geospatial data of wind speeds, and additional land-use constraints to calculate such curves. We find that disamenity costs are not a game changer: for most countries and assumptions, the marginal levelized cost of onshore wind energy increase by 0.2–12.5 €/MWh. KW - Centre for Sustainability Y1 - 2022 U6 - https://doi.org/10.1007/s10640-022-00746-2 ER -