Modelling and Predicting Photovoltaic Power Generation in the EEX Market

Veraart, Almut E. D.; Zdanowicz, Hanna

doi:10.2139/ssrn.2691906

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…As aforementioned, we are keen to build a simple model but sophisticated enough to explain intraday production behavior. Let us recall step by step procedures used in previous papers, as mentioned above (one also can refer to Veraart and Zdanowicz (2016) for a PV analysis). First, we applied a suitable seasonality function to explain the deterministic behaviour of PV production.…”

Section: Methodsmentioning

confidence: 99%

Modelling of Intraday Photovoltaic Power Production

Ibrahim

2021

MJS

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Photovoltaic (PV) productions should occur within a time interval of sunlight. Time mismatches are detected between sunrise and first production hour as well as sunset and last production hour in a transmission system operator, Amprion, Germany. Hence, in this paper, we investigate this effect using an additive function of two seasonalities and a stochastic process. Both seasonalities are based on the mimicked locations, corrected by a weighing scale, depending on the first and last production hours' coordinates. The result shows that the proposed deterministic model could capture the effect of sunrise and sunset. Also, the dynamics of random components are sufficiently explained by an autoregressive process of order two. Finally, the Normal Inverse Gaussian distribution is shown as the best distribution in explaining noise behaviour, particularly heavy tails in the production's residuals, compared to the Gaussian distribution.

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Section: Methodsmentioning

confidence: 99%

Modelling of Intraday Photovoltaic Power Production

Ibrahim

2021

MJS

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“…al [6], Benth &Šaltytė Benth [7], Benth & Taib [10] and Härdle & Lopez Cabrera [20]). A study by Veraart & Zdanowich [28] used trigonometric cyclical function to model PV production. Rather than a potentially high-order sum of trigonometric functions, we propose to use the sun intensity function instead which has a clear physical motivation.…”

Section: Trend and Seasonal Componentmentioning

confidence: 99%

“…Since the moduli of the roots of the autoregressive polynomial are outside the unit circle in all three cases, the fitted AR(3) models are stationary. We note in passing that Veraart & Zdanowicz [28] used an ARMA(2,1) process for the deseasonalized data, where the moving average component is significant.…”

Section: An Autoregressive Modelmentioning

confidence: 99%

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Stochastic modeling of photovoltaic power generation and electricity prices

Benth¹,

Ibrahim²

2017

JEM

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In recent years, renewable energy has gained importance in producing power in many markets. The aim of this article is to model photovoltaic (PV) production for three transmission operators in Germany. PV power can only be generated during sun hours and the cloud cover will determine its overall production. Therefore, we propose a model that takes into account the sun intensity as a seasonal function. We model the deseasonalized data by an autoregressive process to capture the stochastic dynamics in the data. We present two applications based on our suggested model. First, we build a relationship between electricity spot prices and PV production where the higher the volume of PV production, the lower the power prices. As a further application, we discuss virtual power plant derivatives and energy quanto options.

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“…This paper of course is not the first one to consider time series models for longer term PV yields. Models of this kind are already used in Wagner (2014), Veraart and Zdanowicz (2015), Ibrahim (2017) and Benth and Ibrahim (2017), to mention a few. All of them put a special focus on daily mean or maximum PV yields and only use very simple deterministic approaches to obtain hourly PV yields.…”

Section: Introductionmentioning

confidence: 99%

A copula-based time series model for global horizontal irradiation

Müller¹,

Reuber²

2020

Preprint

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The increasing importance of solar power for electricity generation leads to an increasing demand for probabilistic forecasting of local and aggregated PV yields. In this paper we use an indirect modeling approach for hourly medium to long term local PV yields based on publicly available irradiation data. We suggest a time series model for global horizontal irradiation for which it is easy to generate an arbitrary number of scenarios and thus allows for multivariate probabilistic forecasts for arbitrary time horizons. In contrast to many simplified models that have been considered in the literature so far it features several important stylized facts. Sharp time dependent lower and upper bounds of global horizontal irradiations are estimated that improve the often used physical bounds. The parameters of the beta distributed marginals of the transformed data are allowed to be time dependent. A copula-based time series model is introduced for the hourly and daily dependence structure based on a simple graphical structure known from the theory of vine copulas. Non-Gaussian copulas like Gumbel and BB1 copulas are used that allow for the important feature of so-called tail dependence. Evaluation methods like the continuous ranked probability score (CRPS), the energy score (ES) and the variogram score (VS) are used to compare the power of the model for multivariate probabilistic forecasting with other models used in the literature showing that our model outperforms other models in many respects.

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Modelling and Predicting Photovoltaic Power Generation in the EEX Market

Cited by 6 publications

References 20 publications

Modelling of Intraday Photovoltaic Power Production

Modelling of Intraday Photovoltaic Power Production

Stochastic modeling of photovoltaic power generation and electricity prices

A copula-based time series model for global horizontal irradiation

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