Seasonal forecasts of wind power generation

Lledó, Llorenç; Torralba, Verónica; Soret, Albert; Ramón, Jaume Cantallops; Doblas‐Reyes, Francisco J.

doi:10.1016/j.renene.2019.04.135

Cited by 107 publications

(63 citation statements)

References 36 publications

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“…Additional sectors for which S2D forecasts are being assessed for decision-making include agriculture (Klemm and McPherson 2017), energy (demand and wind power generation, Clark et al 2017;Lledó et al 2019), tropical cyclone (Bergman et al 2019) and coastal flooding (Widlansky et al 2017) preparedness, Arctic marine transportation (Stephenson and Pincus 2018), wildfire risk (Turco et al 2019), and food security (Funk et al 2019).…”

Section: E887mentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

162

113

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Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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Section: E887mentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

162

113

View full text Add to dashboard Cite

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“…a few weeks to a few years ahead), the climate modelling community has invested considerable scientific effort and resources in developing probabilistic approaches to subseasonal to seasonal climate predictions 15 . For instance, seasonal forecasts of wind can be used to estimate the capacity factor, which quantifies the impact of weather variability, such as wind, solar radiation or temperature, on production 16 . Probabilistic forecasts of upcoming extremes events could help energy producers, traders, and transmission companies in strategic planning, investment and financial decisions 17 .…”

Section: Incorporating Forecasts In Planningmentioning

confidence: 99%

Better seasonal forecasts for the renewable energy industry

2020

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Anomalous seasons such as extremely cold winters or low wind summers can seriously disrupt renewable energy productivity and reliability. Better seasonal forecasts providing more accurate information tailored to stakeholder needs can help the renewable energy industry prepare for such extremes. The climate mitigation benefits of clean energy come with great challenges. Compared to conventional fossil-fuel based energy sources, renewable sources, such as wind, solar, and hydropower, are highly weather-dependent. Ambitious mitigation policies aiming to reach a 1.5°C target require a radical increase in supplies of clean energy. This implies that total power supply will be more vulnerable to climate-induced weather variability. Balancing the demand and supply of intermittent renewable energy sources thus imposes a big challenge. Consequently, renewable energy technologies must become more resilient to climate variability and high impact events. Weather and climate variability affect the demand as well as supplies of renewable energy. High and low temperatures imply a high energy demand for cooling and heating, respectively 1. The relationship between electricity consumption and temperature is non-linear 2 : at a certain threshold level, an increase in temperatures results in a step increase in energy demand for cooling in summer. For

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“…A point to note is that for accurate representation of the National wind power generation a mean bias correction procedure was required to correct ERA5 to the Global Wind Atlas dataset (GWA, 2018), as in Lledó et al (2019). This is to correct the anomalously low 100m wind speeds found over European land compared to those seen in the Global Wind Atlas.…”

Section: The Era5 Reanalysismentioning

confidence: 99%

“…This is possibly conistent with the perceived difficulty of extracting predicsignals from extended range forecasts (Soares and Dessai, 2016). However, recent advances in forecasting have begun to result in skillful longer range predictions for: European demand (De Felice et al, 2015;Clark et al, 2017;Thornton et al, 2019;Dorrington et al, 2020), wind power generation (Lynch et al, 2014;Beerli et al, 2017;Soret et al, 2019;Torralba et al, 2017;Lledó et al, 2019;Bett et al, 2019;Lee et al, 2019), solar power generation (Bett et al, 2019) and Hydro power generation (Arnal et al, 2018) which can consequently lead to improvements in awareness, preparedness and decision-making from a user perspective (Goodess et al, 2019).…”

mentioning

confidence: 99%

Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European Countries

Bloomfield¹,

Brayshaw²,

Gonzalez³

et al. 2021

Preprint

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Abstract. Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead-times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms and technical barriers frequently prohibit use by non-meteorological specialists. This paper therefore presents data produced through a new EU climate services program Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data corresponds to a suite of well-documented, easy-to-use, self-consistent daily- and nationally-aggregated time-series for wind power, solar power and electricity demand across 28 European countries. The DOI http://dx.doi.org/10.17864/1947.275 will be activated after the paper has been accepted for publication. In the meantime, the data is accessible via https://researchdata.reading.ac.uk/275/, (Gonzalez et al., 2020). The data includes a set of daily ensemble reforecasts from two leading forecast systems spanning 20-years (ECMWF, 1996–2016) and 11-years (NCEP, 1999–2010). The reforecasts containing multiple plausible realisations of daily-weather and power data for up to 6 weeks in the future. To the authors' knowledge, this is the first time fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and the composite property demand-net-renewables is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead-times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy- and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this.

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Seasonal forecasts of wind power generation

Cited by 107 publications

References 36 publications

Current and Emerging Developments in Subseasonal to Decadal Prediction

Current and Emerging Developments in Subseasonal to Decadal Prediction

Better seasonal forecasts for the renewable energy industry

Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European Countries

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