Using quantile regression to analyze the effect of renewables on EEX price formation

Hagfors, Lars Ivar; Paraschiv, Florentina; Molnár, Péter; Westgaard, Sjur

doi:10.1051/rees/2016036

Cited by 24 publications

(9 citation statements)

References 8 publications

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“…In the paper of Hagfors et al (2016b), the UK electricity market is modeled and the dependence of electricity prices on fuel prices and reserve margin is examined. Hagfors et al (2016c) apply the QR to describe the influence of RES on electricity prices in Germany. They analyze hourly data and present the estimates of the wind and solar impact on the electricity prices for a set o quantiles.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Maciejowska

2020

Energy Economics

108

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The literature on renewable energy sources indicates that an increase of the intermittent wind and solar generation affects significantly the distribution of electricity prices. In this article, the influence of two types of renewable energy sources (wind and solar photo voltaic) on the level and variability of German electricity spot prices is analyzed. The quantile regression models are built to estimate the merit order effect for different quantiles of electricity prices. The results indicate that both types of renewable generations have a similar, negative impact on the price level, approximated by the price median. When the price volatility, measured by the inter-quantile range (IQR), is considered, the outcomes show that wind and solar influence prices differently. Conditional on the level of the total demand, the wind generation would either increase (when the demand is low) or decrease (when the demand is high) the IQR. Meanwhile, the increase of solar power stabilizes the price variance for moderate demand level. Thus, policy supporting the development and integration of RES should search for a balance between the wind and solar power.

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Section: Introductionmentioning

confidence: 99%

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Maciejowska

2020

Energy Economics

108

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“…13 Hagfors et al (2016) use quantile regression to address the non-normality of data and regression errors for the EPEX.…”

Section: Robustnessmentioning

confidence: 99%

“…Fanone, Gamba, and Prokopczuk (2013) provide a non-Gaussian model that can generate extreme positive and negative price spikes, and find that incorporating negative prices and spikes is necessary for a good fit of the first 4 moments of German electricity spot prices. Paraschiv, Erni, and Pietsch (2014), De Vos (2015), Hagfors, Paraschiv, Molnar, and Westgaard (2016), and Valitov (2019) provide empirical evidence that links the high incidence of negative prices on the EPEX to the increasing use of electricity production from renewable sources after the German Renewable Energy Act. That evidence attributes negative prices to higher than expected energy production from renewables during nighttime hours of low demand and positive spikes to lower than expected production during daytime hours of high demand.…”

Section: Introductionmentioning

confidence: 99%

Information in Electricity Forward Prices

Michelfelder¹,

Pilotte²

2019

J. Financ. Quant. Anal.

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We examine forward prices in a market where nonstorable inventory exacerbates the influence of seasonal and hourly variation in supply and demand, expected and unexpected, on the level and volatility of spot prices. We find strong evidence, unusual for a commodity, that the difference between contemporaneous forward and spot prices has power to forecast both the spot price change and the risk premium realized at delivery. Our evidence of a time-varying risk premium is consistent with expected hourly and seasonal variation in the needs of producers and retailers of electricity to hedge against extreme spot price decreases and increases, respectively.

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“…By analyzing the impact of wind and photovoltaic on day-ahead spot prices at the EPEX, they conclude that the introduction of renewable energy sources increase the extreme price behaviour and influence the fuel mix for electricity production. Hagfors et al (2016) expand the literature on renewable energy to the German market by looking at the effect of renewable power sources on EPEX price formation using quantile regression. They analyze the effect of wind and photovoltaic, and confirm the results from Paraschiv et al (2014) that renewable energies have a dampening effect on the spot prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of extreme price occurrences in the German day-ahead electricity market

Hagfors

Kamperud

Paraschiv

et al. 2016

Quantitative Finance

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Understanding the mechanisms that drive extreme negative and positive prices in day-ahead electricity prices is crucial for managing risk and market design. In this paper, we consider the problem of understanding how fundamental drivers impact the probability of extreme price occurrences in the German day-ahead electricity market. We develop models using fundamental variables to predict the probability of extreme prices. The dynamics of negative prices and positive price spikes differ greatly. Positive spikes are related to high demand, low supply, and high prices the previous days, and mainly occur during the morning and afternoon peak hours. Negative prices occur mainly during the night, and are closely related to low demand combined with high wind production levels. Furthermore, we do a closer analysis of how renewable energy sources, hereby photovoltaic and wind power, impact the probability of negative prices and positive spikes. The models confirm that extremely high and negative prices have different drivers, and that wind power is particularly important in relation to negative price occurrences. The models capture the main drivers of both positive and negative extreme price occurrences, and perform well with respect to accurately forecasting the probability with high levels of confidence. Our results suggests that probability models are well suited to aid in risk management for market participants in day-ahead electricity markets.

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Using quantile regression to analyze the effect of renewables on EEX price formation

Cited by 24 publications

References 8 publications

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Information in Electricity Forward Prices

Prediction of extreme price occurrences in the German day-ahead electricity market

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