Modeling Swedish real-time balancing power prices using nonlinear time series models

Brolin, Magnus; Söder, Lennart

doi:10.1109/pmaps.2010.5526246

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…If balancing state is modeled implicitly, the balancing state is determined by the sign of the balancing price forecast, and the no-regulation state will only occur if the balancing price is equal to the spot price. Models which explicitly forecast the balancing state include those of Olsson and Söder [17] and Jaehnert et al [12], whereas Boomsma et al [1] and Brolin and Söder [2] use models that forecast the balancing market price without regard to the balancing state. The other main distinction is whether the model takes in exogenous explanation factors or only relies on current and past price information.…”

Section: Model Familiesmentioning

confidence: 99%

“…The other main distinction is whether the model takes in exogenous explanation factors or only relies on current and past price information. A commonly used exogenous explanation factor is the balancing volume, as used by [19], [12] and [2]. Another frequently used explanation factor is the day-ahead market price.…”

Section: Model Familiesmentioning

confidence: 99%

“…When it comes to balancing market prices, no survey exists to our knowledge. However, case studies exist, including Skytte [19], Fleten and Pettersen [9],Olsson and Söder [17], Jaehnert et al [12], Brolin and Söder [2] and Boomsma et al [1] -all of them from the Nordic market. The contribution of this article is a systematic review and benchmarking of time series based methods for balancing market price forecasting.…”

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confidence: 99%

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Benchmarking time series based forecasting models for electricity balancing market prices

2013

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In the trade-off between bidding in the day-ahead electricity market and the real time balancing market, producers need good forecasts for balancing market prices to make informed decisions. A range of earlier published models for forecasting of balancing market prices, including a few extensions, is benchmarked. The models are benchmarked both for one hour-ahead and dayahead forecast, and both point and interval forecasts are compared. None of the benchmarked models produce informative day-ahead point forecasts, suggesting that information available before the closing of the day-ahead market is efficiently reflected in the day-ahead market price rather than the balancing market price. Evaluation of the interval forecasts reveals that models without balancing state information overestimate variance, making them unsuitable for scenario generation.

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Section: Model Familiesmentioning

confidence: 99%

Section: Model Familiesmentioning

confidence: 99%

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confidence: 99%

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Benchmarking time series based forecasting models for electricity balancing market prices

2013

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“…The eectiveness of these approaches, however, is questioned by Kosater and Mosler (2006), who state that there is little evidence that regime-switching methods, even when combined with further statistical forecasting methods, provide added information or forecasting power. Brolin and Söder (2010) propose a non-linear time series model is used to predict realtime Swedish electricity prices coupled with simulation to generate a stochastic scenario tree. The histograms for the simulated data are deemed similar enough to the historical values, so the method has arguably satisfying results for this particular instance.…”

Section: Balancing Market Pricesmentioning

confidence: 99%

Hydropower bidding in a multi-market setting

et al. 2018

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We present a literature survey and research gap analysis of mathematical and statistical methods used in the context of optimizing bids in electricity markets. Particularly, we are interested in methods for hydropower producers that participate in multiple, sequential markets for short-term delivery of physical power. As most of the literature focus on day-ahead bidding and thermal energy producers, there are important research gaps for hydropower, which require specialized methods due to the fact that electricity may be stored as water in reservoirs. Our opinion is that multi-market participation, although reportedly having a limited prot potential, can provide gains in exibility and system stability for hydro producers. We argue that managing uncertainty is of key importance for making good decision support tools for the multi-market bidding problem. Considering uncertainty calls for some form of stochastic programming, and we dene a modelling process that consists of three interconnected tasks; mathematical modelling, electricity price forecasting and scenario generation. We survey research investigating these tasks and point out areas that are not covered by existing literature.

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“…It seems that the low liquidity in most of the BMs and the small number of participants affects the research interest for this type of electricity market. 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%

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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Modeling Swedish real-time balancing power prices using nonlinear time series models

Cited by 6 publications

References 17 publications

Benchmarking time series based forecasting models for electricity balancing market prices

Benchmarking time series based forecasting models for electricity balancing market prices

Hydropower bidding in a multi-market setting

Forecasting balancing market prices using Hidden Markov Models

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