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

Abstract

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.

The application of sub‐seasonal to seasonal (S2S) predictions for hydropower forecasting

“…It is increasingly common to use S2S predictions to create applied forecasts of variables that are impacted by the weather, such as wind, solar, and hydropower output, or energy demand (Bloomfield et al, 2021;Brayshaw et al, 2020;Lled o et al, 2019;. Post-processing techniques for these types of forecast are less well established.…”

“…Post-processing techniques for these types of forecast are less well established. It is common to calibrate the S2S predictions of the weather variables (e.g., wind speed, precipitation, and temperature) using atmospheric reanalysis datasets (Bloomfield et al, 2021;Lled o et al, 2019;. These calibrated predictions (e.g., wind speed) are then applied to secondary statistical or physical models to forecast the impact (e.g., wind power output).…”

“…This calibration step can be conducted with higher spatial resolution datasets, such as atmospheric reanalyses or observations, which reintroduces signals of local-scale variability. The S2S prediction archive is specifically intended to provide easy access to S2S predictions from multiple agencies for forecast evaluation and calibration studies (Vitart, 2017(Vitart, , 2017 and is widely used (Bloomfield et al, 2021;Brayshaw et al, 2020;Olaniyan et al, 2018;Wang & Robertson, 2019). In this study, the coarser resolution of the S2S Prediction Project dataset (150 km) compared with the original model resolution (36 km) may negatively impact our assessment of skill for the un-calibrated precipitation predictions at the case study reservoir due to smoothing of orographic rainfall patterns in the mountainous terrain.…”

Comment on essd-2020-312

“…The impact of a shift to weather-sensitive generation has implications not only for the owners and operators of renewable resources, but across the power system. Skillful forecasts of country-aggregated demand and renewable generation are believed to provide valuable contextual information to a variety of energy system stakeholders: from individual traders, power plant operators and owners, to national transmission system operators (White et al, 2017;Soret et al, 2019). Although the use of short-range weather forecasts is now common in the energy sector and there has been a substantial amount of academic literature on the topic (Bossavy et al, 2013;Füss et al, 2015;Drew et al, 2017;Cannon et al, 2017;Browell et al, 2018;Stanger et al, 2019).…”

“…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).…”