Electric Vehicles: Forecasting, Grid Impacts & Proactive Planning


Electric vehicles (EVs) are rapidly gaining momentum globally, with many countries and automakers setting aggressive EV adoption targets. Rapid EV adoption represents both an opportunity (energy sales growth) and a challenge (infrastructure and demand costs) for electric utilities. Thoughtful forecasting, grid impact analysis, and proactive planning will allow utilities to cost-effectively integrate new EV loads while maintaining high reliability standards. This article provides an overview of key capabilities utilities should develop to properly prepare for wide-scale EV adoption in their service territory.

01 - EV Adoption Forecasting

The first critical capability is developing robust forecasts for EV growth within your service territories. EV adoption is driven by several key factors:

Cost Parity. As battery costs decrease, EVs will reach sticker price parity with internal combustion engine vehicles. Most analysts estimate this will occur between 2025 and 2030.

Vehicle Variety and Availability. As automakers continue to expand the EV models offered, consumers will have more options to consider.

Consumer Preferences. Younger and more affluent consumers often have strong environmental preferences favoring EVs. Regional consumer surveys and publicly available U.S. Census data can provide insights on key population metrics to consider.

Government Policy. Stricter emissions regulations, carbon pricing programs, and purchase incentives boost EV popularity. Monitoring related national, state, and local policies can provide insight to future growth potential.

Charging Infrastructure. Increased public charging station availability alleviates consumer range anxiety, encouraging adoption. Tracking charging infrastructure development provides useful signals on a region’s preparedness for widespread EV adoption.

To develop sound EV forecasts, utilities should analyze historical regional growth trends combined with a holistic analysis of the key points discussed above. Automaker production targets also give visibility into projected EV sales penetration and supply dynamics.

Forecasts should consider multiple scenarios ranging from conservative to aggressive adoption levels. EV sales can be projected as a percentage of total new vehicle sales, with the percentage growing exponentially over time. To translate sales into utility EVs, vehicle lifespan assumptions are needed to calculate retirements. Vehicle registration data can provide a good estimate for the current number of EVs in a given zip code, city, county, or region.

Ultimately, a robust forecast provides visibility into the total number of EVs by year as well as their general locations within the service territory. This fundamental projection serves as the foundation for all subsequent grid impact analysis.


Once EV adoption is forecasted, utilities need to analyze the grid load impacts. This begins by developing load profiles, which estimate the diversified demand from a community of EVs over time. Load profiles are created by simulating driving and charging behaviors using several key variables:

Battery Size. Maximum storage capacity affects required charge times. Larger batteries take longer to recharge.

State of Charge. A fully depleted battery will draw maximum charging current whereas a battery at a higher state of charge typically will draw less current as it reaches 100% charge.

Charger Type. Level 1, 2, and DC fast charging have very different load impacts based on the charger power rating.

Ambient Temperature. Cabin heating and battery conditioning needs increase at extreme temperatures, raising charging loads.

Charging Strategies. Immediate charging upon arriving home creates increased grid demand during peak hours versus delayed charging. Utilities can influence charging behavior with incentives and consumer education.

Charger Availability. The amount of public charging infrastructure is considered when developing and analyzing load shapes.

The assumptions made regarding consumer charging behavior significantly impact the load profiles. Uncontrolled immediate charging after customers return home from work generally creates the highest peak demand. However, utilities have options to mitigate these impacts. Price signals through time-of-use (TOU) rates can incentivize delayed charging, and direct load control allows shifting charging to overnight periods when grid utilization is lower. Additionally, simple consumer education on when to charge your EV has proven effective at influencing consumer charging behavior.

By developing load profiles under various charging behavior scenarios, utilities can quantify the benefits of mitigation strategies and properly prepare for increased EV load.


There are a few key tools that utilities can use to provide incentives to consumers to delay EV charging or shift charging to overnight periods:

Time-of-Use Rates. Utilities can implement time-of-use (TOU) rate plans that charge lower rates overnight and higher rates during peak periods. The price signals incentivize consumers to delay charging to the lower cost overnight hours, while still giving the customer the option to charge their vehicle as needed. TOU rates are enabled by smart meters and often require educating consumers on how their bill is calculated and impacted by different behaviors.

Below are some effective ways that utilities can ensure consumers are aware of TOU rates and impacts:

  • Clear communication when enrolling in TOU rates. Provide information packets that explain the rate periods, prices, and bill impact in simple terms. Have customers affirmatively opt-in to demonstrate understanding.
  • TOU rate comparisons on bills. Show TOU rate prices versus the standard rate on each bill to illustrate savings. Provide regular bill forecasts if possible.
  • TOU usage data on bills. Include charts showing the customer's hourly/daily usage compared to TOU periods to reinforce peak/off-peak behavior.
  • TOU period indicators on smart devices. Leverage smart home devices and apps to provide alerts and notifications when entering peak rate periods.
  • Targeted customer education. Focus educational campaigns on customers with bill variability and low satisfaction to improve TOU understanding.
  • Customer service training. Ensure customer service teams can explain TOU concepts and bill impacts clearly to customers that call with questions.
  • Resources for analyzing bills. Provide bill analyzers, videos, and other self-service resources to help customers understand TOU impacts.

The key is layered, multi-channel communication tailored to different consumer segments. By improving TOU rate awareness, utilities can drive engagement and properly influence charging behavior.

Managed Charging Programs. Utilities can offer rebates or bill credits for allowing direct utility control of EV chargers through a one-way communication link. With direct control of the device, the utility can shift charging to o-peak hours. There is typically a limit to the number of hours or instances in which a utility can directly control or interrupt a charging device, ensuring that consumers are not fatigued by the program. Consumers opt-in to the program and provide control to the utility in exchange for charger rebates or monthly bill credits.

Demand Response Integration. EV chargers can be integrated into demand response programs that utilities use to shave peak loads. During peak events, a signal is sent to enrolled chargers to temporarily reduce charging load, similar to the managed charging programs discussed above. The difference is that demand response events are typically less common and only occur during very specific windows. Consumers receive enrollment incentives and occasional event payments or bill credits.

Public Education. Utilities can educate consumers on the benefits of off-peak charging through websites, social media, and advertising of “Beat the Peak” programs or other similar consumer behavior programs. The idea is to appeal to environmental benefits, doing good for the community, and avoiding grid congestion to help influence behavior.

Workplace Charging Incentives. Another strategy is to provide rebates for employee workplace chargers that have delayed start and/or TOU capabilities, where workplace charging is shifted to the lowest peak hours during the day.

Overall, the combination of financial incentives, technology integration, and public education provides multiple avenues for utilities to encourage off-peak and delayed EV charging.


The next consideration involves integrating EV load profiles into distribution grid models to analyze system impacts. Many utilities have extensive modeling capabilities for the substations, feeders, and transformers that deliver power to customers. The forecasted EV loads can be overlaid onto these existing models.

The geographic allocation of forecasted EVs provides the basis for assigning them to specific feeders and distribution assets. The load profiles are used to grow feeder loads to match each year in the forecast period. EVs can be modeled as clusters of spot loads placed randomly on feeders, or as more uniformly distributed loads along entire feeder segments.

Power flow analysis is then used to simulate load flows with the additional EV loads. This identifies capacity constraints resulting from increased demand. Upgrades may be triggered on substation transformers, feeder conductors, or local distribution transformers in high adoption areas. Low voltages caused by increased peak demand and excessive voltage drops are also identified.

Utilities can augment this standard analysis by developing heat maps that identify areas of the distribution grid that may be more at risk for exceeding capacity. Risk factors like higher home values, multi-family housing, and proximity to public charging stations indicate locations likely to experience high adoption levels. Heat maps enable targeted planning and prioritization of distribution grid upgrades.

By integrating EV load profiles with detailed grid models, utilities gain critical insights into infrastructure risk areas and required system upgrades to maintain reliability.


The forecasting, load impact analysis, and grid modeling capabilities described enable proactive distribution grid planning to cost-effectively manage increased EV adoption. Utilities have several options to mitigate impacts:

Detailed load flow studies allow identifying capacity upgrades to substations, feeders, and transformers needed to accommodate EV loads in a timely manner.

New transformer sizing guidelines for new residential and commercial construction prepare for increased EV density and reduce the need for premature replacements.

AMI systems with transformer-level loading data offer excellent visibility into developing hot spots and indicators of pending overloads.

Coordination with local permit ting offices helps track new residential charger installations to stay ahead of evolving loads.

Consumer education campaigns can encourage safe and code-compliant charger installation along with off-peak charging to manage loads.

Exploring EV time-of-use rate designs creates fair cost recovery and incentives for smart charging behavior.


The mass adoption of EVs presents challenges but also opportunities for utilities focused on grid modernization and clean energy initiatives. Investing in robust forecasts, detailed impact analysis, and proactive distribution grid planning enables utilities to integrate EV loads in a cost-effective manner while delivering value to customers. With careful preparation, utilities can maintain reliable electric service as EVs scale rapidly in coming years.

For more information or to comment on this article, please contact:

KevinMKevin Mara, Executive Vice President
GDS Associates, Inc. - Marietta, GA
678-488-4691 or



jordan_janflone-300x300Jordan Janflone, Project Consultant
GDS Associates, Inc. - Marietta, GA
770-799-2345 or