Electric Vehicle Grid Impact Analysis and Smart-Charge Management

NREL researchers investigate various smart-charge management strategies to optimize the benefits and reduce the risks associated with a widespread increase in electric vehicle (EV) charging.

A hand holding a phone with a charging application running on it, in front of an electric vehicle and a charging station in a parking garage

Integrated EV smart charging can improve grid reliability by more effectively utilizing renewable energy, shaving peak electricity demand, and supporting and maintaining power quality while still meeting the needs of EV drivers. Researchers use smart-charge management controls to improve understanding of EV charging energy and power needs, as well as identify any flexibility in those charging needs that could be used to support energy demand shifts throughout longer vehicle dwell periods. Developing smart-charge management controls that leverage this flexibility will be critical to mitigate the grid impacts of EV charging while accelerating the electrification of the transportation sector.

Identification of Electric Vehicle Adoption and Load Forecasting

NREL researchers are estimating future charging loads for widescale EV adoption based on future projections and current travel patterns across a wide range of vehicle classes and transportation needs. Identifying potential electric vehicle adoption scenarios and their likely charging needs will be critical to analyze the impacts and forecast future loads across the nation’s energy provider service areas.

To estimate these loads, NREL uses the following tools:

TEMPO: Transportation Energy & Mobility Pathway Options Model
Forecasts the adoption of EVs across a wide range of vehicle classes and geographic regions to reflect the different model types, vehicle efficiencies, and charging capabilities.

EVI-X Modeling Suite
Informs the development of large-scale EV charging infrastructure deployments—from the regional, state, and national levels to site and facility operations.

EVI-PRO: EV Infrastructure – Projection Tool
Determines resultant energy needs and vehicle charging needs based on dwell periods, daily travel itineraries, and charge session requirements.

Smart-Charging Strategies

NREL researchers are demonstrating the value of smart-charge management to reduce the impacts of transportation electrification. By employing EV smart-charging strategies, the electricity needs required for charging is distributed across different times throughout the day, which could prevent the power grid from overloading.

Distributed Energy Resources Integration

Integration with distributed energy resources coordinates EV charging with electricity generated from intermittent renewable energy sources.

Communication and Coordination

Communication and coordination between EVs, charging infrastructure, and the power grid optimize charging times and reduce peak demand throughout homes, buildings, and distribution feeders.

Non-Wire Solutions Management

Management of increased demand for electricity without the need for additional investment in traditional power grid infrastructure is known as "non-wire" solutions.

Charging Load Estimation

Estimation of regional charging loads distributes charging more evenly across the grid, thereby reducing peak generation needs, and ensuring safe and reliable charging for all.

Control Adjustment

Adjustment of controls maintains grid reliability during extreme weather events.

Equitable Electric Vehicle Adoption

Using tools, such as the EV Infrastructure for Equity Model, helps develop smart-charge management algorithms that support more equitable and just EV adoption and infrastructure deployment.

Grid Impacts Modeling

NREL researchers are studying the impacts of EV charging on the power grid under various managed charging scenarios using real-world feeder data and state-of-the-art simulations.

To understand these impacts, NREL partners with electric utility companies to share information on distribution feeders and run power flow analysis using tools such as OpenDSS, a distribution system simulator. Researchers also leverage tools such as NREL’s Distribution Transformation Tool (on GitHub) to accelerate the conversion of utility feeder models to OpenDSS and characterize existing assets such as lines, transformers, loads and control devices.

Hosting capacity analysis is performed using NREL’s Distribution Integration Solution Cost Options (on GitHub) tool to assess the existing capabilities of distribution feeders and determine how much additional load the grid can support.

For coordinating all EV and grid analysis, researchers use HELICS (Hierarchical Engine for Large-Scale Infrastructure Co-Simulation) to simulate the future of EV charging, potential resulting impacts on the grid, and effectiveness of smart-charging management to mitigate these impacts by shifting charge sessions.

This analysis can help transportation stakeholders:

  • Optimize construction plans to meet EV drivers’ needs and support future adoption
  • Select managed charging methods to ensure equitable distribution of services and keep costs low
  • Use additional modeling and scenario simulations to aid in decision-making for grid investments
  • Construct and validate various residential transformer, secondary service, and EV charger combinations to measure electric vehicle charging impacts
  • Perform grid analyses on public service feeders.


Andrew Meintz

Chief Engineer for Electric Vehicle Charging and Grid Integration