A weather regime characterisation of Irish wind generation and electricity demand in winters 2009–11

Cradden, Lucy; McDermott, Frank

doi:10.1088/1748-9326/aabd40

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…Cold spells can affect electricity production (Beerli et al 2017;Gruber et al 2021;Doss-Gollin et al 2021) and demand (Cradden and McDermott 2018;Bloomfield et al 2018Bloomfield et al , 2020, human mortality (Charlton-Perez et al 2019, and agriculture (Materia et al 2020a). Similar to heatwaves, cold spells are often defined by temperature and duration thresholds (de Vries et al 2012).…”

Section: B Cold Spellsmentioning

confidence: 99%

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Domeisen

White

Afargan‐Gerstman

et al. 2022

Bulletin of the American Meteorological Society

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Extreme weather events have devastating impacts on human health, economic activities, ecosys tems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on timescales of 3-4 weeks, while this timescale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on timescales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian Oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event - dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

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Section: B Cold Spellsmentioning

confidence: 99%

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Domeisen

White

Afargan‐Gerstman

et al. 2022

Bulletin of the American Meteorological Society

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“…However, high-electricity-demand days and low wind power capacity are related to highpressure systems over Scandinavia and Great Britain [11]. Additionally, during prolonged cold spells, the Irish wind power capacity factors are low while the demand for electricity is relatively high [12]. These examples suggest that there are further benefits in classifying and quantifying the prevalence of different weather systems when balancing supply and demand where there is a large penetration of wind power.…”

Section: Introductionmentioning

confidence: 99%

Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

2021

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Large-scale weather patterns and their variability can influence both the amount of wind power production and its temporal variation, i.e., wind power ramps. In this study, we use a self-organizing map to cluster hourly sea level pressure into a discrete number of weather patterns. The dependency of wind power production and wind power ramps on these weather patterns is studied for the Belgian offshore wind farm fleet. A newly developed wavelet-surrogate ramp-detection algorithm is used for the identification of wind power ramps. It was observed that low-pressure systems, southwesterly and northeasterly wind flows are often associated with high levels of wind power production. Regarding wind power ramps, the type of transition between weather patterns was shown to determine whether ramp up or ramp down events would occur. Ramp up events tend to occur due to the transition from a high-pressure to a low-pressure system, or the weakening of the intensity of a deep low-pressure system. The reverse is associated with ramp down events.

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“…It is a good example of the meteorological situations that could lead to a supply-demand imbalance. Several studies give interesting insights on the typical meteorological large scale situations leading to such extreme events [20][21][22][23]. For instance, in January 2010, Greenland and Scandinavian blocking pattern brought cold temperature and low wind, and associated risk of energy shortage [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies give interesting insights on the typical meteorological large scale situations leading to such extreme events [20][21][22][23]. For instance, in January 2010, Greenland and Scandinavian blocking pattern brought cold temperature and low wind, and associated risk of energy shortage [21][22][23]. Eventually, TSOs did not report any imbalance problem, as the event coincided with winter holidays and a period of economic recession [22].…”

Section: Introductionmentioning

confidence: 99%

Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France

et al. 2020

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Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principal components of large-scale atmospheric predictors. By integrating the joint probability of consumption and production over different areas, we define two kind of risk measures: one quantifies the probablity of deviating from the climatological means, while the other, which is the value at risk at 95% confidence level (VaR95) of the difference between consumption and production, quantifies extreme risks of imbalance. In the first case, the reconstructed risk accurately reproduces the actual risk with over 0.80 correlation in time, and a hit rate around 70–80%. In the second case, we find a mean absolute error (MAE) between the reconstructed and real extreme risk of 2.5 to 2.8 GW, a coefficient of variation of the root mean square error (CV-RMSE) of 3.8% to 4.2% of the mean actual VaR95 and a correlation of 0.69 and 0.66 for winter and fall, respectively. By applying our model to ensemble forecasts performed with a numerical weather prediction model, we show that forecasted risk measures up to 1 month horizon can outperform the climatology often used as the reference forecast (time correlation with actual risk ranging between 0.54 and 0.82). At seasonal time horizon (3 months), our forecasts seem to tend to the climatology.

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A weather regime characterisation of Irish wind generation and electricity demand in winters 2009–11

Cited by 7 publications

References 27 publications

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Advances in the Subseasonal Prediction of Extreme Events: Relevant Case Studies across the Globe

Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France

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