Benchmarking time series based forecasting models for electricity balancing market prices

Klæboe, Gro; Eriksrud, Anders Lund; Fleten, Stein–Erik

doi:10.1007/s12667-013-0103-3

Cited by 66 publications

(82 citation statements)

References 22 publications

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“…This explains why Skytte [39] finds that the balancing price can be explained by the dayahead market price, while Jaehnert et al [40] results indicate no correlation between the spot and balancing prices. Jaehnert et al [40] model the balancing price as the difference to the day-ahead market price, while both Olsson and Soder [41] and Klaeboe et al [42] model it directly including correlation with the spot price.…”

Section: Balancing Pricementioning

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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Section: Balancing Pricementioning

confidence: 99%

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

et al. 2016

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“…The Nordic BM is characterized by a) marginal price settlement: price is set by the marginal cost of the last activated unit, b) hourly settlement resolution, c) dual imbalance pricing scheme: BM price is paid only to the balance responsible players that actively help the restoration of the system balance and d) price cap/floor: DA market prices act as an upper/lower limit to the BM prices [8]. There are three main possible balancing states depending on the direction of the balancing power needed each hour: 1) no regulation state, when consumption equals production, 2) upward regulation state, when consumption exceeds production, and 3) downward regulation state, when production exceeds consumption.…”

Section: Using Hmm For Balancing Market Forecastingmentioning

confidence: 99%

“…This is a highly cited model [6] which uses a combination of a Markov switching state model to predict the state of the BM and a SARIMA model to predict the value of the BM prices. Here a modification of this model is used which combines elements of the models presented in [18] and [8].…”

Section: A the Reference Modelmentioning

confidence: 99%

“…Among the methods that have been proposed for modelling and forecasting electricity prices in the BM, we can find autoregressive models [3]- [5], autoregressive models in combination with Markov processes [6], non-linear time series models [7] and moving weighted average methods [3]. A benchmark of various BM forecasting models is provided in [8].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting balancing market prices using Hidden Markov Models

Dimoulkas

Amelin

Hesamzadeh

2016

2016 13th International Conference on the European Energy Market (EEM)

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Abstract-This paper presents a Hidden Markov Model (HMM) based method to predict the prices and trading volumes in the electricity balancing markets. The HMM are quite powerful in modelling stochastic processes where the underlying dynamics are not apparent. The proposed method provides both one hour and 12-36 hour ahead forecasts. The first is mostly useful to wind/solar producers in order to compensate their production imbalances while the second is important when submitting the offers to the day ahead markets. The results are compared to the ones from Markov-autoregressive model.

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“…Instead, we now highlight samples of statistical approaches for load forecasting from the recent literature, focusing on achieved accuracy and limitations for purposes of scenario generation. Similarly, forecasting models for wholesale market prices have been assessed for their suitability in generating scenarios [13].…”

Section: Introductionmentioning

confidence: 99%

Toward scalable stochastic unit commitment. Part 1: load scenario generation

et al. 2015

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Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra-ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce-narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi-tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method-ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com-mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark. Abstract Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated Disciplines Industrial Engineering | Systems Engineering

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Benchmarking time series based forecasting models for electricity balancing market prices

Cited by 66 publications

References 22 publications

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

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

Forecasting balancing market prices using Hidden Markov Models

Toward scalable stochastic unit commitment. Part 1: load scenario generation

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