Validation of European-scale simulated wind speed and wind generation time series

Murcia, Juan Pablo; Koivisto, Matti; Luzia, Graziela; Olsen, Bjarke Tobias; Hahmann, Andrea N.; Sørensen, Poul Ejnar; Als, Magnus

doi:10.1016/j.apenergy.2021.117794

Cited by 48 publications

(53 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The blended global mesoscale data is further downscaled to a spatial grid of 250 Â 250 m that covers all land (except Antarctica) and 300 km offshore. The high-resolution details of the surface elevation and surface roughness are found to improve the long-term means when compared to observations [85,122]. However, higher resolution does not automatically mean higher quality [117].…”

Section: Hourly To Monthlymentioning

confidence: 80%

See 1 more Smart Citation

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

et al. 2022

Self Cite

View full text Add to dashboard Cite

Section: Hourly To Monthlymentioning

confidence: 80%

“…Another issue is that global reanalyses are relatively smooth and thus tend to exaggerate spatial correlations between neighbouring regions [85].…”

Section: Hourly To Monthlymentioning

confidence: 99%

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

et al. 2022

Self Cite

View full text Add to dashboard Cite

“…Some of these stochastic parameters and sub-systems, particularly in the physical layer, are chronologically dependent, and interactive correlations exist [81,82]. The time-sequence, auto-correlation, and cross-correlation of DERs and loads in the physical layer of the SMG might affect the developed analytical reliability evaluation model, considering various uncertainties [83,84]. The recorded and measured data for stochastic parameters, considering their time-sequence correlation, should be used to generate the state matrices [85].…”

Section: Headermentioning

confidence: 99%

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

View full text Add to dashboard Cite

The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

show abstract

“…Several studies have validated meteorological datasets for modelling weather-dependent wind power generation G. Luzia et al: Spatio-temporal variability in wind time series and its highly fluctuating behaviour. These works use data provided by global atmospheric reanalysis (e.g., Cannon et al, 2015;González-Aparicio et al, 2017;Gruber et al, 2022), mesoscale numerical weather prediction (NWP) models (Murcia Leon et al, 2021;Koivisto et al, 2021), or both (Jourdier, 2020;Murcia et al, 2022). Because mesoscale NWP models cannot represent the effects of the most detailed microscale processes, extra information, such as the effect of the terrain in the wind speed distribution, can be added by combining (i.e., adjusting) mesoscale with microscale data (e.g., Staffell and Pfenninger, 2016;Ruiz et al, 2019;Murcia et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…These works use data provided by global atmospheric reanalysis (e.g., Cannon et al, 2015;González-Aparicio et al, 2017;Gruber et al, 2022), mesoscale numerical weather prediction (NWP) models (Murcia Leon et al, 2021;Koivisto et al, 2021), or both (Jourdier, 2020;Murcia et al, 2022). Because mesoscale NWP models cannot represent the effects of the most detailed microscale processes, extra information, such as the effect of the terrain in the wind speed distribution, can be added by combining (i.e., adjusting) mesoscale with microscale data (e.g., Staffell and Pfenninger, 2016;Ruiz et al, 2019;Murcia et al, 2022). Due to their relatively low temporal resolution (usually available from 30 min to 1 h resolution) and intrinsic numerical smoothing, data from mesoscale models cannot include minute-to second-scale variability, and even hourly variability may be too low compared to measurements (Koivisto et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe

2022

Self Cite

View full text Add to dashboard Cite

Abstract. As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g. energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyse such fluctuations and their spatio-temporal dependencies, time series of wind speeds at an hourly or higher frequency are needed. We provide a comprehensive evaluation of the global and mesoscale-model-derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-spatial-resolution simulations often lower the correlations of wind time series with measurements. Thus, we recommend finer-spatial-resolution simulations for modelling power ramp or voltage stability studies but ERA5 rather than mesoscale simulations for studies where correlations with measurements are essential. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed as estimated from observations. Still, the spatial correlations and the wind speed distributions are insensitive to the mesoscale model configuration tested in this study. Regarding these two metrics, mesoscale model simulations present more favourable results than ERA5.

show abstract

Validation of European-scale simulated wind speed and wind generation time series

Cited by 48 publications

References 92 publications

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe

Contact Info

Product

Resources

About