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Electric Vehicle Smart Charging at Scale

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

Grid-integrated smart charging can improve grid flexibility by more effectively utilizing renewable energy, shaving peak electricity demand, and filling demand valleys while still meeting the needs of EV drivers.

Smart Charging for a Reliable and Resilient Grid

Today, managed EV charging typically reduces peak loads by employing an aggregator that responds to fluctuations in loads across a home or business with one point of interconnection to the utility. Due to the distributed nature of the growing EV load, researchers are exploring new smart-charge-control strategies that respond to load issues throughout an entire distribution feeder or service territory. Such control strategies will enable utilities to continue serving the growing energy needs of EV drivers while mitigating the grid upgrades that widespread uncontrolled charging would otherwise necessitate.

To better understand and quantify the impacts of EV charging, NREL conducted a grid-simulation study in partnership with Sandia National Laboratories and Idaho National Laboratory. Researchers developed distribution-feeder circuit models for the Minneapolis/Saint Paul region to analyze hosting capacity and determine potential load growth from increases in Level 2 charging at homes and workplaces, as well as gas-station-style extreme fast charging.

Next, researchers will analyze the potential impact of projected EV charging in future scenarios for 2025 and 2030. They will use OpenDSS—a grid-modeling software for simulating traditional and advanced distribution technologies, resources, assets, and controls. This will allow the analysis to pinpoint grid improvements required to effectively serve such distributed load increases.

Demand-Charge Mitigation via Stationary Storage

The main advantage of EV fast chargers is their ability to charge vehicles quickly. But, electricity demand charges incurred during peak periods can significantly impact costs for fast-charging applications. As a remedy, NREL is exploring the use of stationary energy storage systems to mitigate demand charges.

Researchers tied a 50-kilowatt fast charger and a 40-kilowatt-hour energy storage system to a building energy meter to investigate how such systems could store energy when building loads are low and discharge energy when they are high. Results indicate that the system successfully alleviates the load increase spurred by EV fast charging by providing energy to the grid, as necessary, to mitigate demand charges.

Charging Station Load Management

NREL is also researching solutions for minimizing the impacts of shared, autonomous EV charging on the power system. Using consensus-based, power-allocation algorithms, researchers are developing distributed control technologies for managing the energy loads of EV-charging station clusters. Charging stations within the same cluster communicate with each other to achieve optimal power sharing through consensus-based algorithms, minimizing peak demand on the power transmission bus.

Automated Electric Vehicles in Ride-Hailing Fleets

NREL is investigating the potential grid impacts of a high concentration of shared, autonomous EVs in ride-hailing fleets. Using HIVE, the Highly Integrated Vehicle Ecosystem simulation framework, researchers are modeling vehicle operations at the city-scale using real-world travel data.

Automated Electric Shuttle in NREL Fleet

Photo of people standing next to a 12-passenger automated vehicle.

NREL's automated electric shuttle bus provides insight into a variety of areas important to the connected, intelligent, and automated vehicle space. Researchers are collecting and analyzing vehicle and charging system operational data to better understand associated energy use, charging and energy storage needs, and autonomous systems operation and control.

Analysis results will help inform the design and optimization of intelligent energy management systems onboard these types of vehicles, such as managed wireless charging or predictive route-based propulsion system control. Additionally, results will feed into NREL’s mobility modeling and energy impacts analyses of connected and automated vehicles and automated mobility districts. Automated mobility districts are implementations of connected and automated EV technologies within a confined region.

NREL is also exploring ways these systems can enable intelligent load management for the entire campus using scenarios with a high concentration of energy coming from renewables or behind-the-meter energy storage.

Peña Station NEXT Zero-Energy Campus

In partnership with Xcel Energy, Panasonic, and Denver International Airport, NREL helped inform the design of the Peña Station NEXT zero-energy campus in Denver. For this transit-oriented project, researchers paired building, vehicle, and grid modeling tools to virtually explore the interaction of the campus with the grid and examine various energy-saving design scenarios.

To accurately integrate EVs into the campus' energy load profiles and better understand related grid impacts, NREL researchers examined detailed driving behavior data and projected EV charging loads. Simulation and analysis results shed light on the value of intelligent EV charging approaches that employ algorithms to align with local energy generation. Researchers identified opportunities to reduce photovoltaic curtailment by 80% and voltage violations by 65% in a zero-energy design scenario that includes 50% EVs versus a scenario without EVs.

Contact

To learn more about NREL's distribution-system-level managed charging research, contact Andrew Meintz.