Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.
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The data utilized or generated in this study have been deposited in figshare, available at https://doi.org/10.6084/m9.figshare.23423051. Raw data of tree canopy can be obtained from https://forestobservatory.com. Grid data can be obtained from https://www.pge.com/en_US/for-our-business-partners/distribution-resource-planning/distribution-resource-planning-data-portal.page and https://drpep.sce.com/drpep. ACS data can be downloaded from https://www.socialexplorer.com. Solar installation data can be downloaded from https://deepsolar.web.app.
Code scripts were developed in Python (v.3.6). The source code is available at https://github.com/wangzhecheng/vulnerability. Required Python packages and their version numbers include: numpy (v.1.19.5), scipy (v.1.1.0), Pillow (v.5.2.0), pandas (v.0.24.2), shapely (v.1.7.1), geopandas (v.0.8.2), geojson (v.2.5.0), scikit-learn (v.0.24.2), statsmodels (v.0.9.0), plotly (v.4.14.3), pyshp (v.2.1.2), torch (v.1.1.0) and torchvision (v.0.2.2).
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This work was funded in part by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technologies Office Fiscal Year 2020 Funding Program (award number DE-EE0009359) to R.R. and A.M., and by a Stanford Precourt Pioneering Project award to R.R. and A.M. The views and opinions expressed by the authors do not necessarily state or reflect those of the funding sources.
The authors declare no competing interests.
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Wang, Z., Wara, M., Majumdar, A. et al. Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid. Nat Energy 8, 1097–1108 (2023). https://doi.org/10.1038/s41560-023-01306-8
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