Vehicle Grid Integration: Can EV Users and the Grid be Reconciled?

Battery-electric vehicles (BEVs) are a key piece of government commitments to midcentury net-zero emissions targets. While BEVs present an opportunity to reduce greenhouse gas (GHG) emissions and air pollution from transportation, they also create a challenge for electric utilities, grid operators and vehicle users and owners. BEVs will create a massive new source of electricity demand, and, to accommodate this, utilities will need to upgrade distribution infrastructure (such as transformers, grid feeders and substations), transmission infrastructure and increase overall electricity generation. Assuming electric vehicles reach a 30% share of global vehicle market, the additional global electricity demand in 2030 will equal the electricity demand from 92 million U.S. homes (73% of U.S. homes) (Sinewatts).

In addition, unmanaged charging of EVs creates inflexible load (i.e., the electricity demand cannot be adjusted to stay within capacity constraints at the site or grid level) and high demand charges when vehicles are charged at times of peak energy demand. Vehicle grid integration turns the electricity demand from EVs into flexible load, and the energy storage component (i.e., the battery) opens the door for additional services and benefits, both to the vehicle user and utility, including storage of intermittent renewable energy, grid services and backup power. Managed charging and bidirectional charging minimize energy costs for EV users and owners by intelligently optimizing charging to ensure vehicles are charged when energy costs are lowest.

Vehicle grid integration marries the transportation and energy markets and involves:

  1. Data-driven planning and prediction of when and where EV load will come from.
  2. Management and optimization of EV charging to limit massive load spikes and high energy costs, while meeting the needs of EV users.
  3. Aggregating EVs to provide grid services (and financial value for EV users and owners), such as demand response, and bid into wholesale energy markets.
  4. Enabling direct use of onsite renewable energy generation for charging.
  5. Preventing renewables curtailment at the distribution and transmission level.
  6. Providing onsite backup power and improve grid resilience.

One study found the typical utility in the US, depending on charging patterns, will need to invest $1,700-5,800 in grid upgrades per electric vehicle through 2030. Although utilities will recoup some of those costs through additional revenue from selling electrons, most of the cost will ultimately be passed on to ratepayers. At the high end of estimates, this will cause a rate increase of 1.4 cents per kWh. To put this number into context, average electricity rates in the U.S. (by state) range from about 9.5 to 32.76 cents per kWh. Most of the costs will occur at the distribution level, and this is also where optimized charging patterns could have the greatest impact on cost by keeping electricity demand within infrastructure capacity. Optimizing both the location of EV chargers and the timing of charging will minimize infrastructure upgrades, and therefore the cost to ratepayers and utilities.

Planning for EV load

Understanding when and where EV load will occur can help utilities optimize the location of chargers, deploy advanced metering proactively (allows variable electricity rates based on time of charging) and develop differential pricing based on location (incentivize charging in some locations, discourage in others). Electricity demand from EVs will not be uniform across locations and geographies and will differ based on EV uptake, vehicle size and charging speed. Utilities need to know when and where EVs will be used and charged, and work with charging network operators, fleets and other charge station operators to place chargers near demand while minimizing distribution infrastructure additions and upgrades (substations, circuits, switches and service transformers).

Managed charging and bi-directional charging

Another key aspect of VGI is charging timing and management. Unmanaged charging means that when a vehicle is plugged in, it consumes energy at a constant rate until it is unplugged. Managed charging involves either reducing the charging rate (amount of energy going to the vehicle at a given time) or delaying charging.

For EV owners and users, including both individuals and fleets, this reduces charging costs, particularly demand charges, and can match up charging with renewable energy generation. For utilities and grid operators, managed charging systems automatically shift EV charging to the right times and places based on pricing and demand signals from the grid. Managed charging is mainly applicable when multiple vehicles are plugged in and unused for long periods of time, such as school buses and employee cars at office building. Other scenarios, such as public fast charging, are not as attractive for managed charging.

Bi-directional charging involves delivering energy from the vehicle back to either a home (vehicle-to-house, or V2H), a building (vehicle-to-building, or V2B) or the grid (vehicle-to-grid, or V2G). The main benefits this provides beyond managed charging are offsetting grid-connected stationary storage and providing grid services in shorter timeframes. Bi-directional charging can also enable EVs to provide backup for a home or building in case of a power outage.

Software-based orchestration platforms

Like I mentioned earlier, VGI sits at the intersection of the transportation and energy markets. As such, many stakeholders are involved: vehicle OEMs, charging station providers, vehicle users and owners and the electric grid operator. For VGI to meet the needs of all stakeholders (particularly vehicle users and electric utility), an orchestration platform, enabled by APIs interfacing with the vehicle battery, charger and utility, needs to ingest, store and process data from all stakeholders to make sure vehicles are charged and ready when needed, while minimizing energy costs.

Cloud-based storage platforms and the data generated by electric vehicles and cars Image Source: Cleantech Group


The solution is purpose-built, application-specific software solutions to coordinate between vehicle user, charger, automaker, stationary batteries and energy source (whether itis onsite solar or utility scale generation), both in the planning phase and when the vehicles are in use. From individual EV owners charging at home to public fast charging networks, transit buses and delivery fleets, EV use cases vary widely and have different infrastructure, charging and operational needs. The following are some key innovators active in the space.

Planning and forecasting

WeaveGrid has developed a software platform to enable scaled deployment of electric vehicles, help utilities plan for when and where EV load will occur, lower infrastructure costs, integrate renewables and lower energy costs for drivers. Recently, WeaveGrid raised a $15 million Series A round led by Coatue with participation from Breakthrough Energy Ventures, The Westly Group, Grok Ventures and several angel investors. The company’s talent includes backgrounds in utilities, tech companies and automotive OEMs.

Fleet management and charging optimization

Bia Power provides grid integration and flexibility services for EVs. The base component of the platform is data management (ingesting and processing data from charging events), which connects to chargers for monitoring and intelligence. The second component is forecasting and prediction models, which use historic data to classify different charging events based on predictability and AI to forecast when charging will happen and how much flexibility will be available with every event. Finally, the platform optimizes charging based on energy costs, peak loads and peak shaving behind the meter, battery health, the cleanest energy mix and grid services. Bia Power’s customers include fleets and parking lots with multiple charging stations.

Electriphi provides an enterprise-focused software platform for fleet and energy management. The solution bridges the gap between fleet operations, including routes and scheduling, and charging management to help fleet operators continue to provide their services while minimizing charging costs and grid upgrades.

Moev provides a machine learning- and cloud-based software platform that gathers data from charging stations, telematics systems, on-site distributed energy assets and grid operators to minimize energy bills for fleets while meeting duty cycles. The machine learning component allows the software to learn about the needs of site host and predict vehicle usage and ‘addressable load’, or how much energy is actually available from each vehicle at a point in time.


Incumbent stakeholders in transportation and energy, including vehicle OEMs, charge station manufacturers, and distributed energy resource (DER) aggregators are building out VGI solutions for electric vehicles to tap into the market. However, EV energy management and grid integration are outside the core competencies of these players. OEMs and charge station manufacturers make money by selling vehicles and charging stations and offering after-sales support, but VGI requires extensive knowledge of energy markets, vehicle use and development of sophisticated software platforms. On the other hand, DER management platforms have extensive knowledge of energy markets and distributed energy, but the EV user is a complicated variable core to VGI and makes it much more complex than just managing a stationary battery or in-home IoT device. Innovators bringing together expertise in energy, EVs and data science and offer purpose-built software solutions will be well-positioned to be leaders in the market.

Keep An Eye On……

In 2020, the global stock of EVs on the road reached 10 million, and nearly one-third of these were new cars registered in 2020. These numbers are expected to rapidly increase in the coming decades, and continued uptake of EVs and buildout of charging infrastructure will accelerate the need for energy planning and management. In addition, energy markets are continuing to evolve, enabling aggregators of flexible load from EVs to participate in wholesale energy markets.

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