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Institute
Residents' acceptance of bioenergy is a prerequisite for its fast and efficient development but is ambiguously discussed in practice and research. We interpret a causal impact of bioenergy plant commissioning on housing prices in their vicinity as the average net external effects for residents. We utilize bioenergy plant construction data in Germany between 2007 and 2022 as a quasi-experiment with naturally occurring control groups in their vicinity and apply recently improved difference-in-difference estimation procedures to analyze instantaneous and heterogeneous treatment effects. We find a significant and negative impact on housing prices if plants use gaseous biofuel, generate electricity on-site, and are medium-sized. The magnitude of the effect is comparable to those for solar fields. In contrast, we find no impact for small and large bioenergy plants and those that use solid or liquid biofuels.
Solar and wind power generation forecasts using elastic net in time-varying forecast combinations
(2022)
Precise renewable energy feed-in forecasts are essential for an effective and efficient integration of renewables into energy systems, and research contributions that help to reduce the uncertainty related to renewables are in high demand. This importance will increase in the future, as renewable energies are the world’s fastest growing electricity generation capacities. Forecast combinations have been empirically proven to outperform individual forecasting models in many disciplines. Our work uses an elastic net method, with cross-validation and rolling window estimation, in the context of renewable energy forecasts. Namely, the forecast combinations are obtained using regional data from Germany for both solar photovoltaic and wind feed-in during the period 2010–2018, with quarter-hourly frequency. The dynamic elastic net estimation, preceded by dynamic data pre-processing, improves forecasting accuracy for both photovoltaic and wind power feed-in forecasts. Moreover, our forecasting framework outperforms benchmarks such as simple average and individual forecasts. Our forecasting framework can be applied widely to estimate renewable power in other countries, systems, or individual power plants.