# Importance subsampling: improving power system planning under climate-based uncertainty

## TL;DR

This paper introduces importance subsampling, a novel method that enables power system planning models to incorporate multi-decade climate variability at high temporal resolution efficiently, improving accuracy and computational feasibility.

## Contribution

The paper presents a new importance subsampling technique that reduces computational costs while accurately capturing climate variability in power system planning models.

## Key findings

- Standard methods cause significant errors in system design estimates.
- Importance subsampling achieves accurate system design with less computational effort.
- Power systems designed with importance subsampling meet demand across all scenarios.

## Abstract

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as "importance subsampling", allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation-based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, "importance subsampling" leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios.