The case of negative day-ahead electricity prices

Fanone, Enzo; Gamba, Andrea; Prokopczuk, Marcel

doi:10.1016/j.eneco.2011.12.006

Cited by 150 publications

(76 citation statements)

References 26 publications

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“…Graphical techniques, like the sample mean excess function, might be used for the selection of a more optimal cutoff level (Fanone et al, 2012); however, this procedure can hardly be automated.…”

Section: Identification Of Spikesmentioning

confidence: 99%

“…The first is to use piecewise constant functions (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). Since this approach yields a non-smooth trend-seasonal component and, hence, requires additional smoothing treatment, it will not be used in this study.…”

Section: Deseasonalizationmentioning

confidence: 99%

“…The price spikes are clearly visible in both cases while the NSW market exhibits a significantly larger number of extreme price observations. Note that the EEX dataset even includes a few price drops with a negative system price (see Fanone et al, 2012, for a discussion).…”

Section: The Data and Their Deseasonalizationmentioning

confidence: 99%

“…There are different suggestions in the energy economics literature for dealing with the trendseasonal component (T t + s t or T t · s t ) of electricity spot prices. 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).…”

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: Identification Of Spikesmentioning

confidence: 99%

Section: Deseasonalizationmentioning

confidence: 99%

Section: The Data and Their Deseasonalizationmentioning

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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“…• piecewise constant functions or dummies, possibly combined with a linear trend (Bhanot, 2000;Fanone et al, 2012;Fleten et al, 2011;Gianfreda and Grossi, 2012;Haldrup et al, 2010;Haugom and Ullrich, 2012;Higgs and Worthington, 2008;Keles et al, 2012a;Knittel and Roberts, 2005;Lucia and Schwartz, 2002),…”

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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Asset management optimization in smart grids

Dashti

Hosseini

2022

Env Prog and Sustain Energy

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Electricity distribution companies manage financial resources to maximize profits.While the stability of the electricity distribution system depends on the efficiency of the resources used by this system. The regulator is responsible for balancing and aligning the stability of the electricity industry with the profits of electricity distribution companies, which does so by adjusting penalty indices and service rates. This article investigates the management indicators of the electricity distribution industry and how to spend each of them is studied by electricity distribution companies and provides a model that can guide the optimization of the division of financial resources and investment between the indicators. Adjust indicators for regulators to align the benefits and profits of electricity distribution companies with the stability of the electricity distribution system. The implementation of this model was observed in Bushehr Electricity Distribution Company and increased the efficiency percentage.

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The case of negative day-ahead electricity prices

Cited by 150 publications

References 26 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

Asset management optimization in smart grids

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