A Copula Function Based Monte Carlo Simulation Method of Multivariate Wind Speed and PV Power Spatio-Temporal Series

Pan, Chaoqiong; Wang, Can; Zhao, Ziyan; Wang, Jinhao; Bie, Zhaohong

doi:10.1016/j.egypro.2018.12.053

Cited by 24 publications

(8 citation statements)

References 10 publications

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“…Some samples of t, Gaussian, Gumbel, Frank, and Clayton copulas are illustrated in Fig. 1 in bivariate form [25]. Consider the joint probability distribution of m-random variables Xi (i = 1, ..., m), H(x1, ..., xm); Where continuous marginal probability distributions are denoted by Fi (xi) = Pr (Xi ≤ xi) = ui.…”

Section: Principles Of Copulas and Dependence 21 Definition Of Copulasmentioning

confidence: 99%

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Short-Term and Mid-Term Load Forecasting in Distribution System Using Copula

Ahmadi

Rivera

2020

Wseas Transactions on Systems and Control

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Short-term and (Mid\Long)-term load prediction are needed for the efficient management of distribution system. An accurate load forecasting could help distribution operators to achieve a great control and flexibility on energy. In addition, a reliable prediction would have significant impact on load power flow problems, operation management, planning, automation, and search market. This paper proposes a new method based on copula for Short-term, and (Mid/Long)-term load forecasting. Proposed method was used less historical data to predict the load consumption then the other methods, so it is useful in distribution system due to scarce installed meters. Copula is a recently developed statistical theory for multivariate probability analysis that has been proposed by Sklar in 1958. Copulas are used in popularity for financial and reliability application. Numerical tests results using database of Iran system’s operation (ISO) shows that this method provides a much better prediction performance in comparison with other methods employing the same data.

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Section: Principles Of Copulas and Dependence 21 Definition Of Copulasmentioning

confidence: 99%

“…The copula selection between the Student's t copula and the Gaussian copula (GC) can be done by comparing the calculated values of the Hannan-Quinn Information Criterion (HQIC) or Akaike Information Criterion (AIC) [24][25][26][27][28][29], which is defined as:…”

Section: Elliptical Copulasmentioning

confidence: 99%

Short-Term and Mid-Term Load Forecasting in Distribution System Using Copula

Ahmadi

Rivera

2020

Wseas Transactions on Systems and Control

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“…To address the shortcomings of the aforementioned works, recent research efforts [16], [17] have developed copulabased models to capture the spatio-temporal correlations of wind time-series data for generating wind power scenarios, with a reduced computational burden. In [16], first a Fourier series expansion is applied to remove any existing trends in the time-series data.…”

Section: Introductionmentioning

confidence: 99%

“…The main limitation of this method is the need to find an appropriate Fourier series expansion for the time-series data. To address this limitation in [17] a Monte Carlo simulation method based on Archimedean copulas is proposed, which is used to generate wind and photovoltaic time-series data, while capturing the spatio-temporal dependence among the data. The accuracy of this model is considerably reduced in higher dimensions, due to inherently poor performance of the Archimedean copulas for dimensions larger than two.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed HMCM produces scenarios of wind farm production with a significant reduction in the total number of copula parameters that need to be estimated. The HMCM is able to capture non-linear temporal dependence among wind time-series data (unlike [10]- [13]), while being a computationally tractable algorithm (unlike [14], [15], [18]) and an effective probabilistic wind modeling for large dimensions (unlike [16], [17]). In addition, the proposed model has a simple structure and does not require training (unlike [19], [20]).…”

Section: Introductionmentioning

confidence: 99%

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Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies

et al. 2020

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An effective model to represent real-world wind power production scenarios is essential for an accurate assessment of the impact of wind power generation on power systems. Such a model should capture the spatial and temporal correlations between different wind turbines that are part of a wind farm. Accurate modeling of these correlations will ensure that the wind power uncertainty can be properly quantified. This is especially critical when analyzing systems with high penetration of wind power productions. This paper addresses this critical need by developing the Hourly Mixed Copula Model, a new probabilistic model, based on Gaussian and Archimedean copulas, to model the uncertainty and variability of wind power generation. This model enables the simulation of realistic scenarios of a wind farm power production. Metrics such as the Euclidean distance and Kullback-Leibler divergence are used to demonstrate the effectiveness of the developed model in incorporating the spatio-temporal dependence among the turbine power productions. The impact of accurate characterization of uncertainty in wind power modeling on power system steady-state operational planning is also demonstrated by case studies. These studies indicate that, in order to correctly evaluate the effect of wind power variation on steady-state operational planning, the dependence among wind power resources should not be neglected for power flow studies.

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