Simulation of aggregate wind farm short-term production variations

Goić, Ranko; Krstulovic, Jakov; Jakus, Damir

doi:10.1016/j.renene.2010.04.005

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…There are examples of simulations carried out at a few sites in sources [51,52]. Source [53] describes an analysis of the Irish power system over a one year period at resolutions of 5, 15, 30 and 60 min.…”

Section: Windmentioning

confidence: 99%

See 1 more Smart Citation

Variability Characteristics of European Wind and Solar Power Resources—A Review

Graabak

Korpås

2016

Energies

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This paper reviews the most recent and relevant research into the variability characteristics of wind and solar power resources in Europe. The background for this study is that wind and solar resources will probably constitute major components of the future European power system. Such resources are variable, and EU plans to balance the variability with more grids and demand response. Thus, planning for the future power system requires an in-depth understanding of the variability. Resource variability is a multi-faceted concept best described using a range of distinct characteristics, and this review is structured on the basis of seven of these: Distribution Long-Term (hours to years), Distribution Short-Term (less than one hour), Step Changes, Autocorrelation, Spatial Correlation, Cross Correlation and Predictable Patterns. The review presents simulations and empirical results related to resource variability for each of these characteristics. Results to date reveal that the variability characteristics of the future power system is limited understood. This study recommends the development of a scheme for greater systematic assessment of variability. Such a scheme will contribute to the understanding of the impacts of variability and will make it possible to compare alternative power production portfolios and impacts of grid expansions, demand response and storage technologies.

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Section: Windmentioning

confidence: 99%

“…However, minute-to-minute variations are less important [47,52]. A possible future power system incorporating large offshore wind farms may, on the one hand, contribute towards reducing the smoothing effect due to wind farm clustering and, on the other hand, increase smoothing as a result of the inclusion of a larger geographical region.…”

Section: Windmentioning

confidence: 99%

Variability Characteristics of European Wind and Solar Power Resources—A Review

Graabak

Korpås

2016

Energies

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“…To synthesize the short-term variation in the generation output of large PV systems connected to a statewide grid, this study estimates their output in hourly and minute-byminute resolutions. In hourly resolution, it is assumed that all PV systems are geographically dispersed across the representative 19 sites across Georgia and have the capacity equal to 10% of the total peak demand, or 17.152 GW in 2010 [6]. The disposition of the cluster of solar farms is depicted in Figure 1.…”

Section: A Large Pv Systems Connected To a Statewide Gridmentioning

confidence: 99%

Impact of Short-Term Variations in the Generation Output of Geographically Dispersed PV Systems

Begović

Kim

Vidakovic³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…In a classic paper, the long-range dependence of the space-time structure of wind over Ireland was examined [39]. A second-order Markov chain model to simulate wind power at two sites simultaneously was developed [40]. An autoregressive framework with temporally varying coefficients for wind vector fields to capture temporal nonstationarity was used [41].…”

Section: Introductionmentioning

confidence: 99%

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

2015

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Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements.

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Simulation of aggregate wind farm short-term production variations

Cited by 26 publications

References 5 publications

Variability Characteristics of European Wind and Solar Power Resources—A Review

Variability Characteristics of European Wind and Solar Power Resources—A Review

Impact of Short-Term Variations in the Generation Output of Geographically Dispersed PV Systems

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

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