EV Load Forecasting for Utilities

Electric vehicle adoption is creating a new utility planning problem: not simply forecasting how much load will grow, but identifying where, when, and how concentrated EV charging load will become on the distribution system.

Many traditional forecasting approaches are useful for estimating broad energy growth. They are often less effective at identifying localized feeder and transformer risks, EV clustering effects, coincident peak impacts, and the practical value of managed charging or other demand-side mitigation strategies.

Why Average Growth Forecasting Misses the Real Problem

EV adoption does not occur evenly across a utility service territory. EV ownership typically first appears in higher-income, single-family,neighborhoods with specific socio-economic characteristics, then clusters geographically. That clustering can create local distribution stress long before total system load growth appears threatening.

A utility may show modest overall EV penetration while still facing overloaded residential transformers, feeder thermal concerns, local voltage issues, or rising coincident peak exposure in specific neighborhoods. A single residential transformer serving only a small group of homes can become stressed by just a few unmanaged Level 2 chargers.

The practical planning question is not only How much EV load will we have? It is where will EV load emerge first, when will it occur, how concentrated will it become, and what is the lowest-cost way to manage it?

Limitations of Many Current EV Forecasting Approaches

1. Top-Down Allocation Methods

Many EV forecasting studies begin with regional EV adoption projections, state forecasts, or system-wide energy growth assumptions. Those values are then allocated downward to substations, feeders, or planning zones using simplified percentages, historical load shares, or broad customer counts.

This can be useful for long-range system planning, but it can also smooth out the exact clustering effects that matter most for distribution planning. Local transformer and feeder exposure can be hidden inside reasonable-looking system averages.

2. Limited Temporal Resolution

Some planning studies still focus on annual peaks, seasonal peaks, or a limited number of critical hours. EV charging behavior is much more time-sensitive. Impacts depend on weekday and weekend charging, charging start times, commuting patterns, work-from-home behavior, weather, time-of-use rates, and managed charging participation.

Without full hourly analysis, utilities may miss peak coincidence effects, secondary overload conditions, and mitigation opportunities that only appear across the full year.

3. Insufficient Customer-Level Modeling

EV adoption is influenced by customer characteristics, including income, dwelling type, vehicle ownership, commuting patterns, and other socio-economic characteristics. Forecasts based only on aggregate growth assumptions are unlikely to realistically capture who adopts EVs first, where adoption clusters, or how quickly local saturation develops.

4. Limited Integration of Mitigation Strategies

Few EV forecasts identify and evaluate future overloads, managed charging, demand response, distributed batteries, residential virtual power plants, and electrification-aware DSM programs within the same modeling framework.

As a result, utilities may struggle to compare conventional infrastructure upgrades with non-wires alternatives using consistent assumptions.

A More Effective Approach: Bottom-Up, Hourly, Location-Sensitive Forecasting

A stronger EV load forecasting approach begins at the customer level. Instead of allocating generalized EV growth downward from the system level, a bottom-up approach estimates where EV adoption is most likely to emerge, how it clusters geographically, and how charging behavior affects hourly loading across the distribution system.

This approach supports feeder-level analysis, transformer-level screening, localized stress identification, and mitigation evaluation before engineering bottlenecks emerge.

• Customer-level EV adoption modeling: estimates EV adoption probability using demographic, housing, and behavioral indicators.
• Hourly load modeling: evaluates full 8,760-hour EV impacts rather than relying only on peak-hour assumptions.
• Geographic resolution: produces results by block group, ZIP code, feeder, transformer, or service area where mapping is available.
• Integrated scenario analysis: tests unmanaged charging, managed charging, DSM participation, VPP strategies, weather extremes, and future housing growth in a common framework.
• Planning-oriented outputs: helps prioritize engineering studies, capital timing, mitigation programs, and distribution investment decisions.

Why This Matters Now

EV forecasting is becoming a distribution planning problem. The issue is no longer limited to estimating aggregate demand. Utilities need to identify localized stress patterns developing inside the distribution system, often years before those risks appear in traditional planning metrics.

Utilities that can identify EV hot spots earlier are better positioned to target engineering review, evaluate managed charging programs, defer or prioritize capital investments, reduce coincident peak exposure, and develop practical electrification strategies.

Where the Grid Impact Model Fits

Jackson Associates' Grid Impact Model is designed to support bottom-up, hourly, location-sensitive EV load forecasting and electrification impact analysis. The model uses customer-level load modeling and scenario analysis to evaluate where EV adoption and other electrification loads may create distribution stress, and how managed charging, DSM, and VPP strategies may reduce those impacts.

The objective is not to replace detailed engineering studies. It is to focus those studies on the right locations, hours, scenarios, and mitigation options.

Conclusion: EV Forecasting Must Become More Local

Traditional load forecasting has focused heavily on energy growth and system peak demand. EV adoption changes the problem. The most important planning risks may appear first at the neighborhood, feeder, or transformer level.

Utilities increasingly need bottom-up visibility, hourly analysis, geographically sensitive forecasting, and integrated mitigation evaluation to support the next generation of distribution planning.