The Importance of Weather and Climate to Energy Systems: A Workshop on Next Generation Challenges in Energy–Climate Modeling

Bloomfield, Hannah; Gonzalez, Paula; Lundquist, Julie K.; Stoop, Laurens; Browell, Jethro; Dargaville, Roger; Felice, Matteo De; Gruber, K.; Hilbers, Adriaan P; Kies, Alexander; Panteli, Mathaios; Thornton, Hazel; Wohland, Jan; Zeyringer, Marianne; Brayshaw, David

doi:10.1175/bams-d-20-0256.1

Cited by 33 publications

(21 citation statements)

References 13 publications

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“…The process of estimating power system behaviour from meteorological data is complex, due to the importance of both meteorological and non‐meteorological factors (see Bloomfield, Gonzalez, et al, 2020b). Due to the added complexities of real‐world power data, we choose to follow an idealized model approach, where national demand and wind power generation created from the ERA5 reanalysis (Hersbach et al, 2020) using established models (see Section 3.1) are taken as truth, and the S2S model performance is compared with this.…”

Section: Datamentioning

confidence: 99%

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Bloomfield

Brayshaw

Gonzalez

et al. 2021

Meteorological Applications

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Sub-seasonal forecasts are becoming more widely used in the energy sector to inform high-impact, weather-dependent decisions. Using pattern-based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern-based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly-mean anomalies of electricity demand and demand-net-wind across 28 European countries. At short lead times (days 0-10) grid-point forecasts have higher skill than pattern-based methods across multiple metrics. However, at extended lead times (day 12+) pattern-based methods can show greater skill than grid-point forecasts. All methods have relatively low skill at weekly-mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern-based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision-making, as it combines the dynamical robustness in the large-scale flow conditions from the pattern-based methods with local information present in the grid-point forecasts.

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Section: Datamentioning

confidence: 99%

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Bloomfield

Brayshaw

Gonzalez

et al. 2021

Meteorological Applications

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“…Additionally, despite the effort of the energy climate community the input data for such studies are not available in a coordinated way for most countries [7]. Using labelled real world data for training an outlier detection method is thus not a viable option, synthetic time series are therefore used within the energy climate community.…”

Section: Related Workmentioning

confidence: 99%

“…However, this method may overlook important weather-related outliers resulting in an inaccurate assessment of reliability under critical conditions. Although energy system experts could complement the method of Hilbers et al with information of extreme events in the past [10], such information is lacking for future weather years from climate models [7]. The latter is crucial though, among others for evaluating the power system performance under climate change conditions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Critical Events in Renewable Energy Production Time Series

Stoop

Duijm

Feelders

et al. 2021

Advanced Analytics and Learning on Temporal Data

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The introduction of more renewable energy sources into the energy system increases the variability and weather dependence of electricity generation. Power system simulations are used to assess the adequacy and reliability of the electricity grid over decades, but often become computational intractable for such long simulation periods with high technical detail. To alleviate this computational burden, we investigate the use of outlier detection algorithms to find periods of extreme renewable energy generation which enables detailed modelling of the performance of power systems under these circumstances. Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power generation time series that have been derived from ERA5 historical climate reanalysis covering the period from 1950 through 2019. By applying the MDI algorithm on these time series, we identified intervals of extreme low and high energy production. To determine the outlierness of an interval different divergence measures can be used. Where the cross-entropy measure results in shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence tends to detect longer and more persistent intervals. These intervals are regarded as potential risks for the electricity grid by domain experts, showcasing the capability of the MDI algorithm to detect critical events in these time series. For the historical period analysed, we found no trend in outlier intensity, or shift and lengthening of the outliers that could be attributed to climate change. By applying MDI on climate model output, power system modellers can investigate the adequacy and possible changes of risk for the current and future electricity grid under a wider range of scenarios.

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“…Accurate quantification of weather and climate risk on scales relevant to energy networks is therefore essential in supporting a rapid transition to low carbon and renewable energy systems, a key COP26 priority theme, though there remain many technical and scientific challenges (Bloomfield et al 2021b).…”

Section: Introductionmentioning

confidence: 99%

Understanding Climate Risk in Future Energy Systems: An Energy–Climate Data Hackathon

Fallon

Bloomfield

Brayshaw

et al. 2022

Bulletin of the American Meteorological Society

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What: Approximately 40 participants – with expertise spanning energy, computer science, weather and climate research -– joined a week-long Energy-Climate data “hackathon” in June 2021. It was hosted by the Universities of Oxford and Reading in partnership with the UK Met Office as part of a series of themed hackathons supported by the Met Office and held in the run-up to the UN COP26 conference. Six projects were initiated and developed by teams over the course of the week, supported by access to state-of-the-art computational resources on the UK’s CEDA-JASMIN service, and stimulated by keynote speakers from industry and academia. The hackathon concluded with teams presenting their outputs to a panel of invited experts. Several teams plan to build on their hackathon success in publications, ongoing collaborations and research funding proposals. When: 18th May (half-day “scoping” event) & 21st-25th June 2021 (main hackathon) Where: Online via Zoom and Gather.Town, supported by Slack communication channels Affiliations: Initiated by: University of Oxford Dr Sarah Sparrow, Professor David Wallom, Professor Tim Woollings, & University of Reading Professor David Brayshaw, Dr Hannah Bloomfield, In partnership with the Met Office, the UK’s national meteorological service, and with support from the UK’s CEDA-JASMIN service and Gurobi optimization software.

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The Importance of Weather and Climate to Energy Systems: A Workshop on Next Generation Challenges in Energy–Climate Modeling

Cited by 33 publications

References 13 publications

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Detection of Critical Events in Renewable Energy Production Time Series

Understanding Climate Risk in Future Energy Systems: An Energy–Climate Data Hackathon

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