Assessing the Shortfall Risk of GB Electricity Grid using Shifts in Winter Weather Conditions

2026-04-22

AI Summary

This study addresses the significant yet often overlooked influence of the interaction between extreme winter weather and calendar effects—such as holidays and weekdays—on the supply–demand balance in Great Britain’s power system. To accurately assess resource adequacy risk under varying date–weather combinations, the authors propose a novel statistical modeling approach that temporally shifts weather sequences while explicitly aligning them with days of the week and proximity to Christmas. Applied to the British power system, this method demonstrates that the perceived severity of the 2010–11 winter can shift from critically high to statistically insignificant depending solely on the alignment of weather with calendar dates. The findings underscore the critical role of weekly periodicity in evaluating extreme weather years and offer a more precise risk assessment framework for power system security planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Graphical Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Extreme weather events during peak winter periods drive resource adequacy risk in Great Britain (GB), with weather sensitivity of the supply-demand balance increasing through additional electric heating and wind generation. This work develops an approach of time-shifting weather within the peak season, through adjustment of the relevant terms in a statistical model for demand. This allows more complete consideration of the security of supply consequences of a weather series, as there will be relevant conditions where demand is suppressed due to weather occurring at a weekend or during the Christmas holiday. Results on a GB example show that consideration of this counterfactual is indeed important, and specifically that winter 2010-11 can either be the most severe in the dataset, or insignificant within the resource adequacy model, depending on the alignment of day-of-week with the weather series. Statistical interpretation of the shift model is discussed, which is straightforward for alignment of day-of-week with weather assuming that all seven alignments are equiprobable; but is more subtle for shifting weather in and out of Christmas, as there is no natural maximum on the realistic length of shift, but too large a shift may be physically unrealistic. It is likely that in all systems, assessment of a weather year's severity is incomplete without such consideration of the day-of-week effect; however, whether longer shifts of weather with respect to date need to be considered will depend on the presence of a major holiday (such as Christmas in GB) in the peak season.

Problem

• shortfall risk
• weather shifts
• resource adequacy
• electricity grid
• winter demand

Innovation

• time-shifting weather
• resource adequacy risk
• demand modeling
• extreme winter events
• day-of-week effect