Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

TLDR

Wind energy is rapidly becoming a major electricity provider, yet utility practice still relies on deterministic forecasts, and the lack of understanding and standardization of uncertainty forecasts hampers their adoption. This paper aims to improve understanding by establishing common terminology and reviewing methods to determine, estimate, and communicate uncertainty in weather and wind power forecasts. The authors review and synthesize existing methods for determining, estimating, and communicating uncertainty in weather and wind power forecasts. The analysis shows that end‑users should evaluate forecast properties to match uncertainty representations to user needs, that multidisciplinary teams are essential, and recommends standardization and operator training with best‑practice examples.

Abstract

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.