The Burrow
Mar 25, 2026
As electric vehicles (EVs) become central to the global shift toward cleaner transport, attention is turning to a new challenge: whether energy networks can keep up with increasing demand.
While EV chargers are becoming more common across cities and highways, the real pressure sits behind the scenes, within the EV grid itself. Rising EV adoption increases electricity demand, and in regions where grid reliability is already under strain, this can intensify outage risks and drive up EV energy cost pressures for households.
As experts in energy comparison, Compare the Market has created the EV Grid Demand & Cost Pressure Index which ranks US states, Australian states and OECD countries based on EV adoption rates, infrastructure readiness and grid reliability using SAIDI (System Average Interruption Index) and SAIFI (System Interruption Frequency Index). The findings reveal where EV growth may place the greatest strain, and where networks appear better prepared.
California ranks highest overall, with an index score of 63.41. The state has by far the highest EV penetration at 31.50 EVs per 1,000 capita, combined with a SAIDI (average outage duration) of 158.20 minutes.
While California’s infrastructure is comparatively advanced, its high EV adoption acts as a proxy for grid pressure relative to reliability. The ranking does not suggest immediate system failure, but it does highlight the scale of demand being placed on the EV grid.
Hawaii follows with a score of 59.16, driven by strong EV uptake (17.56 per 1,000 capita) and comparatively high outage metrics (SAIDI: 225.60 minutes; SAIFI: 2.18). Its geographic isolation and reliance on imported fuel sources add complexity to EV energy affordability and long-term grid resilience.
With an index score of 50.62, West Virginia presents a different risk profile; despite very low EV adoption (1.56 per 1,000 capita), it records extremely high grid strain, with a SAIDI of 486.10 minutes and SAIFI of 2.29. This indicates significant outage vulnerability, meaning even modest growth in EV chargers could cause reliability challenges.
Virginia, ranked ninth with a score of 30.26, shows moderate EV uptake alongside grid reliability figures of 166.40 minutes SAIDI and 1.34 SAIFI – placing it in a transitional category.
Oregon, ranked 10th at 30.18, demonstrates stronger infrastructure readiness and comparatively low electricity pricing, helping offset EV grid stress.
Nevada, with a score of 21.68, appears relatively well positioned. Strong infrastructure and manageable outage metrics suggest the state can absorb additional EV demand more comfortably.
Florida scores 17.45, reflecting moderate EV penetration (10.49 per 1,000 capita) but comparatively lower grid stress, likely supported by diversified generation and energy management strategies.
Queensland ranks the highest nationally with a score of 59.01. EV adoption sits at 7.94 per 1,000 capita, paired with outage figures of 181.00 minutes for SAIDI and 1.50 for SAIFI. The combination of growing EV uptake and moderate grid reliability places Queensland at the top of the national risk profile.
Tasmania also records elevated grid strain, with a SAIDI of 167 minutes and SAIFI of 1.61. While adoption levels remain lower than Queensland, reliability metrics indicate vulnerability if EV charger demand accelerates rapidly.
Victoria scores just 24.75, despite relatively strong EV adoption (7.07 per 1,000 capita). Its moderate outage figures suggest a comparatively more stable EV grid environment.
South Australia, with a score of 34.02 and EV penetration of 5.78 per 1,000 capita, sits in the mid-range, reflecting healthy adoption and grid reliability.
The results suggest that while Australia’s EV adoption remains lower than leading US states, reliability differences between regions could influence how smoothly networks accommodate future demand.
When it comes to countries facing the most pressure, Norway ranks highest globally, with EV penetration at 20.08 per 1,000 capita and a grid stress score of 54.55. Despite very low outage figures (SAIDI: 1.20; SAIFI: 1.42), Norway’s ranking reflects a proxy measure: extremely high EV adoption relative to reliability. Importantly, this dataset does not account for Norway’s advanced EV charging infrastructure or policy frameworks. Instead, it highlights the scale of adoption relative to baseline reliability metrics.
Türkiye, scoring 52.85, shows a contrasting profile. With EV penetration at just 1.20 per 1,000 capita, high grid vulnerability is driven primarily by infrastructure limitations rather than demand pressure.
Belgium scores 43.29, supported by strong reliability metrics (SAIDI: 0.55; SAIFI: 0.51) and moderate EV adoption.
Denmark, at 41.15, combines high EV penetration (15.95 per 1,000 capita) with low outage frequency, placing it in a moderate pressure category.
Japan sits at the bottom of the index at 2.09, supported by low EV sales (0.85 per 1000 capita) and having the lowest outage of all countries (SAIDI: 0.17; SAIFI: 0.06).
Mexico also records one of the lowest scores globally at 3.13, with very low EV adoption (0.20 per 1,000 capita) and relatively low strain metrics (SAIDI: 0.56; SAIFI: 0.99), indicating capacity for future growth.
Meredith O’Brien – Head of Energy at Compare the Market, explains that the findings highlight how EV adoption and grid resilience must evolve together. She adds, “As electric vehicle adoption continues to grow, it’s essential that the EV grid keeps pace. Our index doesn’t suggest certain states are at risk of immediate failure. Rather, it shows where adoption is highest relative to grid reliability, which can act as a pressure point over time.
“In places like California and Norway, high EV uptake is a positive sign of transition, but it also means careful energy planning is crucial. Meanwhile, states such as West Virginia show how older infrastructure can become a vulnerability even before EV adoption accelerates.
“With EVs being huge consumers of electricity, it’s essential that EV owners who charge their car at home choose an energy plan that helps ease electricity costs pressures, not adds to them. However, EVs are also great opportunities to store electricity, and for owners to optimise their solar generation or take advantage of off-peak rates or specialised EV charging rates.
“Understanding the benefits and risks of EV expansion can help you understand how grid reliability can change and be better prepared for potential disruptions to your energy supply.”
This dataset ranks U.S. states, Australian states/territories, and a set of selected countries globally based on their potential exposure to electricity grid strain from electric vehicle (EV) adoption.
By combining EV adoption per capita with real-world grid reliability metrics, the index highlights where EV-related charging demand is likely to grow fastest in regions that already experience more frequent or longer power outages. These areas may be more exposed to future grid stress as EV adoption accelerates.
Each factor’s data was collected and normalised to a score between 0 and 1. If data was missing, a score of 0 was given. These scores were then combined to give each region a total score out of 100, and regions were ranked from highest risk to lowest risk.
The factors used were:
1) EV Adoption (per capita)
EV registrations were collected for each region and normalised per capita using population figures. This measures where EV uptake is most concentrated relative to the size of the population.
Global EV adoption note (countries)
For the global country index, EV sales were used as the indicator, rather than total EV stock/registrations. This is because consistent EV registration totals were not available across all countries in a comparable format.
Selected countries were included based on data availability. Population data for the global index was sourced from the same dataset for consistency.
2) Grid Reliability
Grid reliability was measured using SAIDI and SAIFI, two standard electricity distribution performance metrics:
SAIFI: average number of outages per customer per year.
SAIDI: total duration of outages experienced per customer per year.
Higher SAIDI and SAIFI values indicate weaker grid reliability.
Comparability note (global vs state-level)
SAIDI and SAIFI values are not always reported using identical methodologies across all sources. In particular, U.S. and Australian datasets typically report SAIDI/SAIFI using national regulatory reporting standards.
Global country-level data is sourced from the World Bank, which provides international comparability but may differ in underlying reporting definitions.
Additionally, SAIDI is a minutes-based measurement, and reporting standards can vary by country and regulator. For this reason, global comparisons should be interpreted as directional rather than perfectly equivalent.
Weighting
Factors were weighted as follows:
EV adoption per capita: 50%
Grid reliability (SAIDI + SAIFI combined): 50%
Grid reliability was calculated by averaging the normalised SAIDI and SAIFI scores.
Indexing rules
Data sources (US)
Data sources (Australia)
Data sources (Global countries)
Links
US
Australia
Global