As electric vehicles (EVs) continue their steady rise in popularity, questions emerge about whether local electricity networks are ready to support them — particularly during peak charging periods. In population-dense Brisbane and broader South East Queensland (SEQ), EV growth puts added pressure on zone substations.
A new study led by Arche Energy’s Abel Quintero focuses on assessing how these substations perform under increasing EV loads. In their published paper, Abel and his co-authors Mark Hickman and Jake Whitehead, combine real-world load data with observed EV charging patterns to develop a web-based planning tool. The tool helps network operators and policymakers identify which substations are well-equipped for growing EV demand and which could be at risk.
The study examines 221 zone substations across SEQ by using historical load data to evaluate how EV charging impacts each site under different conditions. Charging patterns were drawn from Queensland-based data (Energex) and a complementary South Australian study (Philip et al). The analysis covered both typical daily behaviour and high-demand scenarios. Its findings offer an early warning system for identifying substations that may need upgrades or demand-management strategies as EV use grows.
Why zone substations matter
Zone substations serve as the link between the subtransmission network and the high-voltage distribution system. They supply multiple neighbourhoods via downstream transformers. Each has a Normal Cyclic Capacity (NCC). This represents the maximum load the substation can safely handle under regular conditions. It also has an Emergency Cyclic Capacity (ECC), which allows short-term overloads when part of the equipment is offline.
Managing these substations becomes increasingly complex with the addition of EV charging. Charging tends to peak in the late afternoon and evening, which coincides with existing household energy use. Even though each individual charger is relatively small, the combined impact of hundreds or thousands will be substantial.
This study’s strength lies in its dual focus: examining standard charging habits and modelling worst-case peaks. It doesn’t just ask if substations can handle the average day — it asks what happens when many EVs charge at once, especially during equipment outages.
The data and the methodology
The tool developed in this study draws entirely from real operational data and publicly available geographic information. Three main data sources were used:
- Substation load data: Energex’s zone substation load data reports provided baseline demand and NCC/ECC capacity limits for each of the 221 substations.
- Vehicle numbers: Car counts by SA1 census areas from the ABS were used to estimate how many vehicles are likely to charge within each substation’s reach.
- Charging patterns: Four different time-of-day demand curves were taken from existing Queensland studies. These include both “home only” and “home+away” behaviours to capture realistic usage scenarios.
To map EVs to substations, the study applied a 300-metre buffer around each low-voltage transformer and placed randomly generated vehicle points within each SA1 area to represent local car ownership. These were aggregated to determine how many EVs would charge from each substation.
The model then adds estimated EV charging demand to each substation’s historical load data, across various scenarios. It then computes the percentage of time the combined load exceeds NCC or ECC.
Users can adjust:
- The percentage of vehicles that are electric (eg, 30%, 70%, or 100% EV uptake)
- The type of charging behaviour (eg, home only or a mix of home and away)
- The concurrency rate (what share of EVs are charging at once).
This flexible structure allows planners to test both gradual transitions and extreme peaks.
What the results say
One of the most encouraging findings is that under typical charging behaviour — even at 100% EV uptake — most substations remain within safe operating limits. Depending on the profile used, 78% to 94% of substations stayed below their emergency thresholds (ECC) for more than 99% of the year. Only one substation in the entire network exceeded its normal threshold (NCC) for more than 0.05% of the year.
However, the picture changes when considering simultaneous charging. When 5% of vehicles in an area charge at the same time using 7.4kW chargers, one substation breaches its NCC. At 10% concurrency, four substations exceed safe limits. At 25%, the number of substations exceeding their capacity rises sharply. This illustrates that it’s not just the number of EVs on the road that matters, but when and how they charge.
Restricting home chargers to 4.6kW during the 4–9pm peak window reduces the number of substations exceeding NCC across all concurrency levels. For example, at 10% simultaneous charging it falls from 4 to 1 substation (a 75% reduction) and at 20% from 37 to 22 substations. This demonstrates that time-of-use limits can meaningfully cut overload risk without major network upgrades.
Planning implications and what comes next
The tool developed in this study offers a data-driven method for assessing EV-readiness at the substation level. The findings suggest that most of SEQ’s substations can accommodate substantial EV growth with no upgrades, assuming charging behaviour remains relatively spread out throughout the day.
However, a small but critical subset of 48 out of 221 substations were flagged for further review. These sites may require targeted infrastructure upgrades or operational changes. This is particularly important under high-concurrency charging conditions or during n–1 contingencies, when part of the substation equipment is offline.
Rather than broad network upgrades, the study shows that managing demand can curb overloads more cost-effectively. In Queensland, any charger above 32 A (~7 kW) must use economy or controlled tariffs that restrict charging during the 4–9pm peak period. Meanwhile, single-phase home chargers are naturally capped at 20A (~4.6kW), limiting instantaneous load. Together, these targeted measures such as controlled tariffs, output caps, and peak-period restrictions shift charging away from high-demand windows without major infrastructure works.
The authors also outline opportunities to expand the tool’s capabilities. Planned enhancements include analysing spot electricity prices, incorporating rooftop solar adoption data, and developing optimal charging strategies to reduce grid stress and emissions. While vehicle-to-grid (V2G) coordination is not part of the tool’s next stage, the paper highlights it as a charging mode worth further investigation due to its impact on user behaviour and grid interaction.
This Brisbane case study offers more than a static view of grid performance. It provides a practical framework for identifying substation-level stress points using real-world data. The tool itself is clear, adaptable, and ready to use. It helps network planners take early, informed action before problems emerge.
EVs are coming — and fast. But with tools like this, Australia’s electricity network can prepare smartly, affordably, and with confidence.
Based entirely on: “Zone substations’ readiness to embrace electric vehicle adoption: Brisbane case study.”
