Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast’s properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

As the penetration levels of renewable energy sources increase and climatic changes produce more and more extreme weather conditions, the uncertainty of weather and power production forecasts can no longer be ignored for grid operation and electricity market bidding. In order to support the energy industry in the integration of uncertainty forecasts into their business practices, this work describes an experiment conducted with 105 participants from the energy industry. In the framework of an IEA Wind Task 36 workshop, the experiment aimed to investigate existing psychological barriers in the industry to adopt probabilistic forecasts and to better understand human decision processes. We designed and ran a ‘decision game’ to demonstrate the potential benefits of uncertainty forecasts in a realistic—although simplified—problem, where an energy trader had to decide whether to trade 100% or 50% of the energy of an offshore wind park on a given day based on deterministic and probabilistic uncertainty day‐ahead forecasts. The focus thus was on a decision‐making process dealing with extremes that can cause high costs in the form of security issues in the electric grid for system operators, or high monetary losses for traders, who have bid a power production into the market that failed to be produced due to high‐speed shutdown of the wind turbines. This paper presents the obtained results, extracts behavioural conclusions and identifies how to overcome psychological barriers to the adoption of uncertainty forecasts in the energy industry.

The current grid code in Ireland requires wind farms to deliver meteorological data to the two transmission system operators. The importance of accurate on‐site measured wind speed signals increases as a function of the installed wind capacity. Since wind farms have been installed with varying technologies over the past 20 years, the system does not have a single point of failure. The lack of uniformity has been found to be both a handling challenge and reliability strength. In order to investigate the future compatibility of the current quality of MET data and to verify, whether and which types of new technology may be an acceptable source of meteorological data to be delivered to the TSOs, a study has been carried out. The study verified whether the quality of meteorological data is sufficient and reliable enough for the 2030 penetration targets set by the Irish government. This paper will describe the methods used to verify the quality of meteorological data signals, along with the various types of meteorological measurements that were found to be acceptable under varying conditions. The authors will also present several recommendations based on the findings of the study, and discuss how these recommendations are being implemented by EirGrid.