Climate Data for Overhead Line Resilience to extreme Events

(climate adaptation, the topic of this paper). There are particular issues for power systems due both to the long lifetime of assets (many planned today will be operating in the late 21st century) and the criticality of power supply to the whole of society. This is recognised in Great Britain

(GB) at industry level in network companies’ climate resilience strategies, and at national policy level in the UK Climate Change Risk Assessment (CCRA).

Resilience analysis for overhead lines presents particular challenges, as these are linear assets which experience a wide range of ambient weather conditions along their length. This brings a need both for appropriate spatial weather and climate datasets, and complicates calibration of these datasets as there is unlikely to be historic ground truth data available at all relevant locations. This paper presents research on application of climate datasets to these questions in a GB context, with the general approach being applicable to other systems and data sources. The analysis is based on best practice from the climate community in selection and use of datasets, as summarised early in the paper.

Four specific applications are then described. First, through statistical exploration of relevant climate data, we show that in GB the very highest temperatures are increasing more rapidly under climate change than more moderate temperatures, emphasising the need for climate metrics and analysis to be specialised to the very high temperatures which are of particular concern. The use of appropriately calibrated climate data as an input to physical plant modelling is then demonstrated for study of physical degradation of overhead line conductors; this concludes that for the example in question, the relevant high line loadings are experienced sufficiently rarely that the higher ambient temperatures anticipated in future years will not cause substantial degradation through this mechanism.

The final part of the paper presents further visualisations of climate data that can support resilience decision making. The first of these shows how use of ensembles of climate model runs can demonstrate substantial variability of temperature outcomes over an extended period of years based on the same assumptions about drivers of climate change. An interactive visualisation tool, designed to allow a wider range of analysts in the industry to create spatial representations of climate data according to their own needs, is then demonstrated – the code for this is engineered to allow visualisation of alternative data sources with little additional development effort, further facilitating provision of bespoke information to analysts.