What Happens When It’s Windy in Denmark? An Empirical Analysis of Wind Power on Price Volatility in the Nordic Electricity Market

Mauritzen, Johannes

doi:10.2139/ssrn.1754931

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…By contrast, wind power increases the daily price volatility in Germany. However, our results are aligned with those of Jónsson et al (2010), Mauritzen (2010), and Ketterer (2014). In Denmark, the price-decreasing impact of wind power is distributed evenly during different times of day, and there is a peak in average wind power production during peak hours.…”

Section: Discussionsupporting

confidence: 82%

“…By adopting the same time-series framework as in Mauritzen (2010), Mauritzen (2013) investigates how wind power affects the cross-border transmission of electricity between Denmark and Norway. His conclusion is that when more (less) wind power is produced in Denmark, exports to (imports from) Norway are higher while Norwegian hydropower plants produce less (more).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to explaining the results of Ketterer (2014) and Mauritzen (2010), our approach of dividing the data into off-peak and peak hours contributes to the literature on estimating the impact of renewable generation on electricity price levels (see Würzburg et al (2013), Mulder and Scholtens (2013), Paraschiv et al (2014), andGelabert et al (2011), for example) by providing insights on how the price-decreasing impact is distributed during the day. To this end, Barthelmie et al (1996) and Holttinen (2005) suggest that Danish wind power peaks in the afternoon and the effect is more pronounced in summers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Our methodology is largely based on Mauritzen (2010) who represents the volatility of prices via a seasonal autoregressive moving average (SARMA) model in which wind power production is an exogenous variable. This methodology yields results that are straightforward to interpret and makes it possible to develop forecasts for electricity price volatility based on the data from previous days and information on regular consumption patterns.…”

Section: Introductionmentioning

confidence: 99%

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Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Rintamäki¹,

Siddiqui

Salo³

2017

Energy Economics

167

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Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts price volatility. Because the flexibility to respond to high peak and low offpeak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.

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Section: Discussionsupporting

confidence: 82%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Rintamäki¹,

Siddiqui

Salo³

2017

Energy Economics

167

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“…Therefore, by displacing higher marginal cost electricity generation, one could expect some level of decrease in the electricity spot market prices. This fact is reported by several authors (Amorim et al, 2010;Cruz et al, 2011;Cutler et al, 2011;Gelabert et al, 2011;Jónsson et al, 2010;Klessmann et al, 2008;Mauritzen, 2010;Moreno et al, 2012;Mulder and Scholtens, 2013;Sáenz de Miera et al, 2008;Sensfuß et al, 2008) and implies the hypotheses of increasing the cross-border transit of electricity, therefore market splitting.…”

Section: Introductionmentioning

confidence: 52%

Evaluating the market splitting determinants: evidence from the Iberian spot electricity prices

2015

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Assess determinants on market splitting behaviour of Iberian electricity markets. Logit and non-parametric models to express market splitting probability response. Explanatory variables: wind, hydro, thermal and nuclear power; ATC and demand. Results: increase of market splitting probability with higher availability of low marginal cost electricity. Coordination policies governing both interconnections and renewables deployment. a b s t r a c tThis paper aims to assess the main determinants on the market splitting behaviour of the Iberian electricity spot markets. Iberia stands as an ideal case-study, where the high level deployment of wind power is observed, together with the implementation of the market splitting arrangement between the Portuguese and the Spanish spot electricity markets.Logit and non-parametric models are used to express the probability response for market splitting of day-ahead spot electricity prices as a function of the explanatory variables representing the main technologies in the generation mix: wind, hydro, thermal and nuclear power, together with the available transfer capacity and electricity demand. Logit models give preliminary indications about market splitting behaviour, and then, notwithstanding the demanding computational challenge, a non-parametric model is applied in order to overcome the limitations of the former models.Results show an increase of market splitting probability with higher wind power generation or, more generally, with higher availability of low marginal cost electricity such as nuclear power generation.The European interconnection capacity target of 10% of the peak demand of the smallest interconnected market might be insufficient to maintain electricity market integration. Therefore, pro-active coordination policies, governing both interconnections and renewables deployment, should be further developed.

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Risk mitigation in the electricity market driven by new renewable energy sources

Krecar

Gubina

2019

WIREs Energy & Environment

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An important indicator of supply and demand uncertainty on electricity markets is risk premium (RP), an integral part of electricity price. The level of RP is describing the risk the market actors expect in the future due to the market uncertainties. With the electricity market price behavior rapidly changing, the rising market supply uncertainty is increasing the volatility of RP. To better understand this behavior, power market participants need new models that will efficiently use the information available in the processes driving the electricity price and RP and to explain the influence of RES uncertainties on the future RP and electricity prices. A decade ago, researchers investigating RP focused primarily on the uncertainties arising from consumption forecast and generation outages. RES share in generation mix was small and its influence on uncertainty was negligible, leading to much lower volatilities of RP than today. With the increasing influence of RES, typically exhibiting variability on a sub-hourly level, traditional models using daily electricity price to calculate RP were becoming inadequate. In this dynamic period of electricity market transformation, this paper highlights the importance of RP signals to market actors. A stochastic method for RP calculation is discussed with the associated RP model, driven by the intraday dynamics. An example of the RP signal is presented on historical price data from the German electricity market, highlighting uncertainty pattern developed over the years. With such an approach, the market actors can adjust their trading strategies thus mitigating their market risk exposure.

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What Happens When It’s Windy in Denmark? An Empirical Analysis of Wind Power on Price Volatility in the Nordic Electricity Market

Cited by 13 publications

References 7 publications

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Evaluating the market splitting determinants: evidence from the Iberian spot electricity prices

Risk mitigation in the electricity market driven by new renewable energy sources

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