Optimising the use of ensemble information in numerical weather forecasts of wind power generation

Abstract

Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility. In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind electricity generation output forecast uncertainty and is extremely valuable at informing wind energy trading strategy.

How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?

“…Addressing this issue would require stratifying the forecast systems that enter the multimodel and looking at the difference in behavior of the slope and convexity parameters and the residual. Finally, the impact on reliability of other bias correction and calibration techniques could be investigated, such as quantile mapping (Déqué 2007;Themeßl et al 2011), member-by-member bias correction directly from the rank histograms (Stanger et al 2019), calibration through subensemble selection (Herger et al 2018), or other memberby-member calibration approaches such as nonhomogeneous Gaussian regression (Van Schaeybroeck and Vannitsem 2015).…”

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

“…Skilful 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;Browell et al, 2018;Stanger et al, 2019), there has been comparatively little attention paid to the use of sub-seasonal to seasonal (S2S) forecasts by energy users for decision-making. This is possibly consistent with the perceived difficulty of extracting predictable signals from extended-range forecasts (Soares and Dessai, 2016).…”

“…This is possibly consistent with the perceived difficulty of extracting predictable signals from extended-range forecasts (Soares and Dessai, 2016). However, recent advances in forecasting have begun to result in skilful 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 hydropower 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).…”

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

“…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). There has been comparatively little attention paid to the use of sub-seasonal to seasonal (S2S) forecasts by energy users for decision making.…”