TY - GEN A1 - Müsgens, Felix A1 - Grothe, Oliver T1 - The Influence of Spatial Effects on Wind Power Revenues under Direct Marketing Rules T2 - Energy Policy N2 - In many countries, investments in renewable technologies have been accelerated by fixed feed-in tariffs for electricity from renewable energy sources (RES). While fixed tariffs accomplish this purpose, they lack incentives to align the RES production with price signals. Today, the intermittency of most RES increases the volatility of electricity prices and makes balancing supply and demand more complicated. Therefore, support schemes for RES have to be modified. Recently, Germany launched a scheme which gives wind power operators the monthly choice to either receive a fixed tariff or to risk a – subsidized – access to the wholesale electricity market. This paper quantifies revenues of wind turbines under this new subsidy and analyzes whether, when and where producers may profit. We find that the position of the wind turbine within the country significantly influences revenues in terms of EUR/MWh. The results are important for wind farm operators deciding whether electricity should be sold in the fixed feed-in tariff or in the wholesale market. However, no location is persistently, i.e., in every calendar month of the year, above the average. This limits the effect of the new subsidy scheme on investment locations and long term improvements in the aggregated wind feed-in profile. Y1 - 2013 U6 - https://doi.org/10.1016/j.enpol.2013.03.004 SN - 0301-4215 IS - 58 SP - 237 EP - 247 ER - TY - CHAP A1 - Käso, Mathias A1 - Müsgens, Felix A1 - Grothe, Oliver T1 - Dynamic Forecast Combinations of Improved Individual Forecasts for the Prediction of Wind Energy T2 - IEEE Conference Proceedings EEM 2016 N2 - We study the prediction performance of different improved individual wind energy forecasts in various static and dynamic ombination processes. To this end, we develop a combined error minimization model (CEMM) based on nonlinear functions. This approach reflects the nonlinear nature of weather and especially of wind energy prediction problems. Based on the model, we construct significantly improved individual forecasts. The corresponding time dependent model coefficients are determined by dynamic OLS (ordinary least squares) regression and Kalman filter methods. The former method shows a slightly better performance than the Kalman filter based approaches. Further improvements can be achieved by a combination of these improved wind energy forecasts. In this case, the combination coefficients are calculated from a static and two dynamic OLS regressions. The resulting forecasts are characterized by a further increased prediction accuracy compared to the combination of the uncorrected forecast data and can outperform a given benchmark. Y1 - 2016 UR - http://ieeexplore.ieee.org/document/7521228/ SN - 978-1-5090-1298-5 U6 - https://doi.org/10.1109/EEM.2016.7521228 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Möbius, Thomas A1 - Watermeyer, Mira A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - Enhancing energy system models using better load forecasts T2 - Energy Systems N2 - Since energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators’ day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators’ load forecast data in real-time, using only the load forecast error’s history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data. KW - Data pre-processing KW - Day-ahead electricity prices KW - Energy system modelling Y1 - 2023 U6 - https://doi.org/10.1007/s12667-023-00590-3 SN - 1868-3975 SP - 1 EP - 30 ER - TY - GEN A1 - Watermeyer, Mira A1 - Möbius, Thomas A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling T2 - arXiv N2 - The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2304.09336 SP - 1 EP - 38 ER -