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

Nowotarski, Jakub; Tomczyk, Jakub; Weron, Rafał

doi:10.1016/j.eneco.2013.04.004

Cited by 75 publications

(65 citation statements)

References 52 publications

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“…Also predicting the wavelet LTSC beyond the next few weeks is a difficult task, since individual wavelet functions are quite localized in time or (more generally) in space. Preliminary research suggests, however, that despite this feature the wavelet LTSC can be extrapolated into the future yielding an on-average comparable prediction of the level of future spot prices to that of an extrapolation of a sinusoidal LTSC (Nowotarski et al, 2011). An alternative, potentially promising approach would be to use forward looking information, like smoothed forward curves.…”

Section: Forecasting Of the Seasonal Componentsmentioning

confidence: 99%

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: Forecasting Of the Seasonal Componentsmentioning

confidence: 99%

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

et al. 2012

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“…Other use sinusoidal functions or sums of sinusoidal functions [18,36]. Wavelet decomposition and smoothing is another possibility that is less sensitive to outliners and less periodic [36,37]. Wavelets offer a very good in-sample fit to the data, but wavelets ability to forecast is poor [38].…”

Section: The Deterministic Componentmentioning

confidence: 99%

Investment in electric energy storage under uncertainty: a real options approach

et al. 2016

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Problem descriptionThe growing portion of renewables in the generation mix has led to an increasing need for ancillary services in the electricity grid. As a part of this green shift, the role of storage in the electricity market has changed. Traditionally, the motivation for investing in storage was based on time arbitrage of the spot price. Today, the investments in storage are being linked with the need for improving the power quality and balancing the grid.Recent research papers point out that investments in small storage facilities are not profitable today without public support. This thesis will apply the real options framework, and investigate the profitability of energy storage under uncertain electricity prices, balancing prices and investment cost. It will further consider how policy makers can trigger investments in electric energy storage. PrefaceThis is a master thesis written within the field of Financial Engineering at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). We would like to thank our supervisors, Verena Hagspiel and Stein-Erik Fleten, for professional guidance throughout the project. We also wish to thank Professor Sonja Wogrin at Universidad Pontificia Comillas de Madrid for technical assistance, PhD candidate Lars Ivar Hagfors at NTNU for valuable input and Statkraft for providing market data. Trondheim, 8th of June 2015Ida BakkeBeate Norheim AbstractThe transition from conventional power sources to renewable energy sources is taking place in a number of European countries. Electric energy storage has been proposed as an environmentally friendly solution to make this transition possible. This thesis analyzes the profitability of investing in a battery bank in Germany and the UK, using a real options model. The model determines the option value and the optimal investment time, under the conditions of uncertain revenues and investment cost. The results show that it is profitable to invest in both countries, given that the battery banks can participate in both the spot and balancing market. The valuation also gives insight into how the battery bank should be operated between the two markets to maximize its expected profits and discovers that the battery earns over 70 % of its profit from ancillary services. This finding underlines the importance for investors to not only consider revenues from the spot market. The thesis further analyzes how uncertainty affects investor behavior and explains why there is a reluctance to invest in storage technology under the current market conditions; the investor is favoring the option to wait for more information. SammendragOvergangen fra konvensjonelle energikilder til fornybare energikilder finner sted i en rekke europeiske land. Elektrisk energilagring har blitt foreslått som en miljøvennlig løsning forå gjøre denne overgangen mulig. I denne masteroppgaven utvikler vi en realopsjonsmodel for a analysere lønnsomheten vedå investere i batteribanker i Storbritannia og Tyskland. Mode...

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“…Alternatively, some smoothing technique like wavelets or moving average can be used (Weron 2009;Nowotarski et al 2013). The mean-reverting property is typically modeled with some mean-reverting processes, like e.g.…”

Section: The Modelmentioning

confidence: 99%

Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach

Janczura

2013

Math Meth Oper Res

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In this paper we derive analytic formulas for electricity derivatives under assumption that electricity spot prices follow a 3-regime Markov regime-switching model with independent spikes and drops and periodic transition matrix. Since the classical derivatives pricing methodology cannot be used in the case of non-storable commodities, we employ the concept of the risk premium. The obtained theoretical results are then used for the European Energy Exchange data analysis. We calculate the risk premium in the case of the calibrated 3-regime MRS model. We find a time varying structure of the risk premium and an evidence for a negative risk premium (or positive forward premium), especially at short times before delivery. Finally, we use the obtained risk premium to calculate prices of European options written on spot, as well as, forward prices.

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

Cited by 75 publications

References 52 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

Investment in electric energy storage under uncertainty: a real options approach

Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach

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