Space-Time Trajectories of Wind Power Generation: Parametrized Precision Matrices Under a Gaussian Copula Approach

Tastu, Julija; Madsen, Henrik

doi:10.1007/978-3-319-18732-7_14

Cited by 35 publications

(28 citation statements)

References 39 publications

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“…In [13], the wind power generation is explained at several locations from space-time trajectories including paths sampled from high-dimensional joint predictive densities. In [14], a multi-objective optimization programming is employed in an integrated building and microgrid system.…”

Section: Introductionmentioning

confidence: 99%

CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids

Tavakoli

Shokridehaki

Akorede

et al. 2018

International Journal of Electrical Power & Energy Systems

181

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This paper aims at enhancing the resilience of a photovoltaic-based microgrid equipped with battery storage, supplying a typical commercial building. When extreme weather conditions such as hurricane, tsunami and similar events occur, leading to islanding of the microgrid from the main power grid, it is not expected that the microgrid would be taken out of service for a long time. At the same time, it is not cost effective to make the electrical system to be absolutely reliable to provide service for the customers. The main contribution of this paper lies in its ability to determine the optimal point between the operational cost and grid resilience. In other words, this work proposes an optimal management system of battery energy storage in a way to enhance the resilience of the proposed microgrid while maintaining its operational cost at a minimum level. The optimization is achieved by solving a linear optimization programming problem while the Conditional Value at Risk (CVaR) is incorporated in the objective function. The CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. Simulation analyses are carried out in MATLAB to evaluate the performance of the proposed method. Results reveal that the commercial building microgrid resilience is improved remarkably at a slight increase in the operational cost.

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Section: Introductionmentioning

confidence: 99%

CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids

Tavakoli

Shokridehaki

Akorede

et al. 2018

International Journal of Electrical Power & Energy Systems

181

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“…Using multiple wind farms as 'spatial sensors' was shown to improve wind power forecast skill at a target site in [16]. Recent contributions have sought to build efficient probabilistic spatial models with sparse Gaussian random fields, but are limited to modest spatial dimension [17], [18]. However, with the abundance of wind farms on many power systems today it is desirable to build a spatial predictor for tens, or hundreds, of wind farms, making computational cost and automated model fitting serious considerations.…”

Section: Introductionmentioning

confidence: 99%

Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression

Dowell

Pinson

2015

IEEE Trans. Smart Grid

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183

115

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Link back to DTU OrbitCitation (APA): Dowell, J., & Pinson, P. (2016). Very-short-term wind power probabilistic forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), 763 -770. DOI: 10.1109/TSG.2015 Abstract-A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters. The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates numerical advantages over conventional vector autoregressive models. The proposed method is tested on a dataset of 5 minute mean wind power generation at 22 wind farms in Australia. 5-minute-ahead forecasts are produced and evaluated in terms of point and probabilistic forecast skill scores and calibration. Conventional autoregressive and vector autoregressive models serve as benchmarks.Index Terms-Probabilistic forecasting, wind power, power system operations, renewable energy.

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“…An exponential covariance function, like in [8], is used in the Gaussian copula. Finally, Tastu et al [9] extend this framework to generate joint predictive densities of wind power output (and ultimately, spatial-temporal trajectories). The proposed method also relies on the Gaussian copula for the dependency structure and takes advantage of the sparsity of precision matrices (i.e., inverse covariance matrix), accounts for non-constant conditional variances and directiondependent conditional dependencies.…”

Section: Introductionmentioning

confidence: 99%

On the quality of the Gaussian copula for multi-temporal decision-making problems

Bessa

2016

2016 Power Systems Computation Conference (PSCC)

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Multi-temporal decision-making problems require information about the potential temporal trajectories of wind generation for a given time horizon. Typically, the Gaussian copula is used for modelling the dependency between probabilistic forecasts from different lead-times. This paper explores the vine copula framework as a benchmark model since it captures complex multivariate dependence structures with mixed types of dependencies. The results show that a Gaussian copula with a suitable covariance matrix suffice to generate high quality temporal trajectories.

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Space-Time Trajectories of Wind Power Generation: Parametrized Precision Matrices Under a Gaussian Copula Approach

Cited by 35 publications

References 39 publications

CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids

CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids

Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression

On the quality of the Gaussian copula for multi-temporal decision-making problems

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