Compressed-air energy storage power plant investments under uncertain electricity prices: an evaluation of compressed-air energy storage plants in liberalized energy markets

Keles, Dogan; Hartel, Rupert; Möst, Dominik; Fïchtner, Wolf

doi:10.21314/jem.2012.070

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…Independent spike (IS) MRS models, where the base regime is modeled by an AR(1) process and the spike regime by an independent normal (log-normal or Pareto) random variable, were introduced by Huisman and de Jong (2003) and Weron et al (2004a). In the following years various MRS models have enjoyed extensive use due to their relative parsimony and the ability to capture the unique characteristics of electricity spot prices (Bierbrauer et al, 2007;De Jong, 2006;Erlwein et al, 2010;Haldrup et al, 2010;Higgs and Worthington, 2008;Hirsch, 2009;Huisman, 2009;Weron, 2010, 2012;Kanamura andŌhashi, 2008;Karakatsani and Bunn, 2008;Keles et al, 2012;Kholodnyi, 2005;Mari, 2008;Mount et al, 2006;Weron, 2009).…”

Section: Stochastic Models For the Deseasonalized Spot Pricementioning

confidence: 99%

“…For instance, some authors use piecewise constant functions (or dummies) for the months (Bhanot, 2000;Fanone et al, 2012;Fleten et al, 2011;Haldrup et al, 2010;Higgs and Worthington, 2008;Knittel and Roberts, 2005;Lucia and Schwartz, 2002). Other model the seasonal pattern by sinusoidal functions or sums of sinusoidal functions of different frequencies (Bierbrauer et al, 2007;Borovkova and Permana, 2006;Cartea and Figueroa, 2005;Erlwein et al, 2010;Geman and Roncoroni, 2006;Keles et al, 2012;Lucia and Schwartz, 2002;Pilipovic, 1998;Seifert and Uhrig-Homburg, 2007;Weron, 2008), sometimes coupled with an exponentially weighted moving average (EWMA) (De Jong, 2006). As a more robust to outliers and less strictly periodic alternative to Fourier analysis, wavelet decomposition and smoothing have been applied by Weron (2010, 2012), Stevenson (2001), Stevenson et al (2006), Weron ( , 2009 and Weron et al (2004a,b).…”

Section: Introductionmentioning

confidence: 99%

“…While it is clear that price spikes should be captured by an adequate stochastic model, like mean reverting jump-diffusion (Bierbrauer et al, 2007;Borovkova and Permana, 2006;Cartea and Figueroa, 2005;Clewlow and Strickland, 2000;Geman and Roncoroni, 2006;Jabłońska et al, 2011;Nomikos and Soldatos, 2010;Seifert and Uhrig-Homburg, 2007;Weron, 2008) or a regimeswitching model (Becker et al, 2007;De Jong, 2006;Higgs and Worthington, 2008;Hirsch, 2009;Huisman and Mahieu, 2003;Weron, 2010, 2012;Keles et al, 2012;Mari, 2008;Mount et al, 2006;Weron, 2009), the literature does not agree on whether these observations have to be included or excluded in the estimation of the seasonal pattern.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

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Section: Stochastic Models For the Deseasonalized Spot Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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“…• sinusoidal functions or sums of sinusoidal functions of different frequencies (Benth et al, 2012;Bierbrauer et al, 2007;Cartea and Figueroa, 2005;De Jong, 2006;Erlwein et al, 2010;Geman and Roncoroni, 2006;Keles et al, 2012b;Lucia and Schwartz, 2002;Pilipovic, 1998;Seifert and Uhrig-Homburg, 2007;),…”

Section: Introductionmentioning

confidence: 99%

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

2013

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When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less important for valuation of typical power derivatives, the long-term seasonal components (LTSC; seasonal, annual) are much more difficult to tackle. Surprisingly, in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered. With this paper we want to fill the gap and present a thorough study on estimation and forecasting of the LTSC of electricity spot prices. We consider a battery of models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that all considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long-term seasonal components.

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“…• sinusoidal functions or sums of sinusoidal functions of different frequencies (see e.g. Benth et al, 2012;Bierbrauer et al, 2007;De Jong, 2006;Keles et al, 2012;Weron, 2008),…”

Section: Introductionmentioning

confidence: 99%

A note on using the Hodrick–Prescott filter in electricity markets

Weron

Zator

2015

Energy Economics

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Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine-based models. However, a clear disadvantage of the wavelet-based approach is the increased complexity of the technique as compared to the other two classes of LTSC models, which may render it too complicated for practitioners. To facilitate this problem, we propose here a much simpler, yet equally powerful method for identifying the LTSC in electricity spot price series. It makes use of the Hodrick-Prescott (HP) filter, a widely-recognized tool in macroeconomics.

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Compressed-air energy storage power plant investments under uncertain electricity prices: an evaluation of compressed-air energy storage plants in liberalized energy markets

Cited by 20 publications

References 30 publications

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

A note on using the Hodrick–Prescott filter in electricity markets

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