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This study demonstrates improvements in ensemble wind forecasts at hub height due to bias correction strategies and their impact on wind energy forecasts at the Alta II wind farm in southern California. The ensemble consists of twenty members that differ in physics schemes used. Ensemble wind forecasts are produced for three months. Hub-height sodar wind observations are used to evaluate forecast performance. Time-dependent bias correction (TBC) and probability bias correction (PBC) are proposed to calibrate hub-height ensemble wind forecasts. The root mean square errors (RMSEs) and biases of forecasted hub-height winds are significantly reduced using both bias correction methods. RMSEs were reduced by 20% and 15% for TBC and PBC, respectively. When evaluating forecast performance from a reliability perspective, PBC better corrects both high and low forecast probabilities for high-wind thresholds. The penalty associated with deviation of the wind energy forecasts from observed values is reduced by 8.7% and 8.0% for TBC and PBC, respectively. It is notable that PBC produces a greater penalty reduction during the high wind bias forecast period (6 p.m.–8 a.m. local standard time); during this time period, PBC reduces the penalty by 25.8% while TBC reduces it by just 19.2%.

The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.The talk discusses the general setup of the Task, and the latest developments. Among those are decision making under uncertainty. To this aim, a series of forecasting experiments are being developed and one initial experiment was tested by a wide audience. The forecasting experiments took the form of a game, during which the participants could experience the benefit of probabilistic information on their decisions to trade. Other results include an information portal for meteorological data, and the IEA Recommended Practice for Forecast Solution Selection which is divided into 3 parts: (1) "Forecast Solution Selection Process", (2) "Designing and Executing Forecasting Benchmarks and Trials", and (3) "Evaluation of Forecasts and Forecast Solutions". The Recommended Practice guideline encourages forecast users to establish a framework of metrics that help identify, whether the user's forecast performance criteria effectively incentivize the forecast provider to optimize towards the forecast target variable that has the most value for the user's application(s). For this year, we intend to update the guideline in the light of the experiences throughout the industry in its initial application, and after collecting this experience at 3 Open Space workshops.Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating.The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP also currently organises a benchmark for NWP models, based on the Wind Forecast Improvement Project 2 (WFIP2) datasets. WP2 deals with the power conversion from the wind speed forecasts and the associated vendor issues. Amongst other things, WP2 published the IEA Recommended Practice on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts and also what kind of measurements are required in real-time environmentsA major activity of the Task is the organisation of workshops and special sessions at conferences, like this one. Previous workshops on e.g. forecasting on the minute scale including lidars, a workshop on the value of forecasts, or special sessions on the Wind Energy Science Conference, the Wind Integration Workshop, the ESIG Meteorology and Market Design for Grid Services workshops are still visible online from the IEAWindForecasting YouTube channel.

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations for deploying new resources. Planning for fuel delivery and maintenance relies on subseasonal to seasonal forecasts. On shorter timescales of days, the weather affects both energy demand and supply. Electrical load depends critically on weather because electricity is used for heating and cooling. As more renewable energy is deployed, it becomes increasingly important to understand how these energy sources vary with atmospheric conditions; thus, predictions are necessary for planning unit commitments. On the scales of minutes to hours, shortterm nowcasts aid in the real-time grid integration of these variable energy resources (VERs).

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integration of solar energy. Chief among them will be developing new tools and practices that manage the variability and uncertainty of solar power.

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

With the higher levels of wind power and other renewable generation now included in the North American electrical system, operational information plays an increasing role in the decisions that grid operators are asked to make on a daily basis. While wind forecasting was something that was considered a luxury only a few years ago, it is critical to today's operation. There are other solutions that the industry is developing to incorporate high levels of wind, such as demand-side management, developing flexible resources to accommodate ramping and cycling needs, and developing larger BAs and market tools to allow the development of ancillary services. But wind forecasting will always be a crucial part of the solution and the primary tool that grid operators will rely on to implement policies and procedures to reliably and economically operate the system.