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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.
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.