The impact of renewable energies on EEX day-ahead electricity prices

By Florentina Paraschiv, David Erni, Ralf Pietsch. 2014

278 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/bf51c41ef161669e5c5bbb109c6ea4455377d7ad

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

The unstudied barriers to widespread renewable energy deployment: Fossil fuel price responses (2017, background): “A review of the literature (Browne et al., 2015; Clò et al., 2015; De Vos, 2015; Dillig et al., 2016; Paraschiv et al., 2014; Pöyry, 2010; Würzburg et al., 2013) shows that, thanks to its low marginal cost, the deployment of this type of renewable generators tends to reduce electricity wholesale spo”
Probabilistic mid- and long-term electricity price forecasting (2017, methodology): “Paraschiv et al. (2014) or Ziel et al. (2015). These processes can be partitioned into two groups.”
The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts (2018, methodology): “This parameter was likewise used by Uniejewski et al. (2018) and yielded good results across several markets. The time series is a quarter-hourly one which renders a slight transformation necessary. Daylight saving time causes one duplicate hour as well as a missing value. We follow Weron (2007) and”
Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany (2017, background): “Indeed, Paraschiv et al. (2014) find that the impact of wind power on German prices has been up to 3.5 times higher in the morning off-peak than in the peak hours, but the difference has decreased over time.”
Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, background): “, [2]–[4]) or a regime switching model (e.”