The increasing impact of weather on electricity supply and demand

TLDR

Rapid cost declines in wind and solar power have accelerated their deployment, yet their output variability remains a key challenge, and as other sectors electrify, the shape and variability of electricity demand will also change. The authors develop an open framework to quantify weather impacts on electricity supply and demand using the Renewables.ninja and DESSTINEE models. The framework employs these models, is demonstrated on Britain’s Two Degrees scenario to 2030, and is globally applicable with limited input data such as annual quantities from government scenarios. The study shows Britain’s peak demand will exceed 70 GW by 2030, renewable output will outstrip demand from 2021, hourly ramp‑rates will widen by 50 % and year‑to‑year variability will rise 80 %, underscoring the need for multi‑year weather data in future power system studies.

Abstract

Wind and solar power have experienced rapid cost declines and are being deployed at scale. However, their output variability remains a key problem for managing electricity systems, and the implications of multi-day to multi-year variability are still poorly understood. As other energy-using sectors are electrified, the shape and variability of electricity demand will also change. We develop an open framework for quantifying the impacts of weather on electricity supply and demand using the Renewables.ninja and DESSTINEE models. We demonstrate this using a case study of Britain using National Grid's Two Degrees scenario forwards to 2030. We find the British electricity system is rapidly moving into unprecedented territory, with peak demand rising above 70 GW due to electric heating, and intermittent renewable output exceeding demand as early as 2021. Hourly ramp-rates widen by 50% and year-to-year variability increases by 80%, showing why future power system studies must consider multiple years of data, and the influence of weather on both supply and demand. Our framework is globally applicable, and allows detailed scenarios of hourly electricity supply and demand to be explored using only limited input data such as annual quantities from government scenarios or broader energy systems models.