Joint price and volumetric risk in wind power trading: A copula approach

Pircalabu, Anca; Hvolby, Thomas; Jung, Jesper; Høg, Esben

doi:10.1016/j.eneco.2016.11.023

Cited by 41 publications

(23 citation statements)

References 33 publications

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“…(2016), Pircalabu et al (2017) and Rintamäki et al (2017), among many others. It is worth emphasizing that most of the authors have modelled each hourly time series individually (that is 24 hourly time series separately), as in Misiorek et al (2006) and in García-Martos et al (2007), hence, ignoring the relationships among different hours of the day.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

Gianfreda

Ravazzolo

Rossini

2020

International Journal of Forecasting

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This paper compares alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregressive specifications, for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark, Italy and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

Gianfreda

Ravazzolo

Rossini

2020

International Journal of Forecasting

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“…For example, [9] examines another type of copulas families to handle the dependency between wind speed and their directions [10] utilize a pair copula (conditional dependence) to analyze the correlation between the winds farms, while [11] use the extremes value theory and copulas function to determines the correlation between wind turbines that compose the wind farm. Others [12] examine the dependence between wind power production and electricity prices.…”

Section: Introductionmentioning

confidence: 99%

Modeling Wind Energy Using Copula

Bahraoui¹,

Bahraoui²,

Bahraoui³

2018

OALib

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In most studies related to wind energy, the quantity of the air density is considered constant, but actually, we know that it is variable and depending on others natural factors. We present a new procedure to estimate the wind density power energy by simulating the components of the air density. The procedure uses the copula theory and demonstrates that the estimated power energy is higher if the air density is not constant.

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“…Frequently, this dependency is neglected by the decision modeler when constructing the probabilistic model, which may result in errors during the probabilistic assessment, e.g., calculation of risk metrics [161]. The copula theory to construct joint distributions and pairwise correlations to incorporate dependence among the random variables is one possible method [162].…”

Section: Uncertainty Modeling and Integration In The Decision-making mentioning

confidence: 99%

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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Joint price and volumetric risk in wind power trading: A copula approach

Cited by 41 publications

References 33 publications

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

Modeling Wind Energy Using Copula

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

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