Efficient modeling and forecasting of electricity spot prices

By Florian Ziel, R. Steinert, S. Husmann. 2014

127 citations recorded by the source corpus.

The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and load as influences on the time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for the estimation. Moreover, it is shown that several existing models are outperformed by the procedure developed in this paper.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ec24ee4fadded81988587fbd4fce1e2d4591ad12

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In Electrical and Electronic Engineering

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Citations in the corpus

Electricity price forecasting using sale and purchase curves: The X-Model (2015, methodology): “Ziel et al. (2015b) show that the very simple naive estimator (cid:98) X price d,h = X EXAA,price d,h with X EXAA,price d,h as EXAA electricity price at day d and hour h is very competitive.”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, methodology): “This method is fast and does not require a distributional assumption as Evans et al. (2014), Ziel et al. (2015) and Ambach and Croonenbroeck (2016) point out.”
Probabilistic mid- and long-term electricity price forecasting (2017, background): “Similarly as in the short term model in Ziel et al. (2015) the extreme upper quantiles (98% and 99%) are overrepresented as well.”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, methodology): “Ziel et al. (2015) proceed similarly for modeling the wind and solar power net feed-in.”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, methodology): “A fourth option is to periodically update the hyperparameter but using information criteria, e.g. the Akaike information criterion (AIC) or the Bayesian information criterion [64,68,121].”
Electricity prices, large-scale renewable integration, and policy implications (2017, background): “The empirical consideration of negative electricity prices for the case of the German/ Austrian electricity market is rarely found in the literature since they were not present until 2009 (Ziel et al., 2015).”
Econometric modelling and forecasting of intraday electricity prices (2018, background): “The latter ones are well-described in the literature, see e.g. Ziel et al. (2015a).”
Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, methodology): “It is used by [10] and [23] for electricity price forecasting.”