Dan Garvey, Contributor
Only one percent of the vehicles on the road today are Electric Vehicles (EVs). This limited number of cars, trucks and buses pose no immediate threat to the nation’s grid integrity. But what happens as these numbers grow? What risks will utilities encounter as millions more EVs connect to the grid for their essential charge?
The simple answer is” it depends. Primary factors include the geographic concentration for EVs and the ability of utilities to effectively model their performance and incentivize charging behavior to avoid the need for system upgrades. But one certainty is clear: utilities must plan to meet the demand created by a possible surge in EV adoption. Assessing the potential impact of EVs and how to mitigate these changes, including potential equipment damaged by EV charging, is essential to ensuring grid fidelity.
First, let’s look at adoption trends. A recent Edison Electric Institute report (EEI) on EV adoption and projected market penetration over the next decade outlines a steady growth in EV sales and their overall on road percentage of passenger vehicles in the US. The projected increase of EVs will exceed 18 million vehicles by 2030, or approximately seven percent of the cars and light trucks on the road.
But these numbers can be misleading. We also need to add to the mix the electrification of transport such as buses, trains and even battery-powered scooters now joining bike-sharing in many large cities. Shenzhen China, for example, now has 14,000 electric transit buses while New York City has only 53. Consider that Los Angeles has a goal of converting all passenger vehicles to electric by 2050 and has plans to add more than 150,000 charging stations. By then electricity demand nationwide could be 10 times what it is today.
The looming EV revolution is likely to drive a power grid metamorphoses. Our century-old business models will be upended. Action must be taken now; power company executives must proactively manage this game changer.
Geographic concentration: where the EVs are
The concentration of where EVs are likely to be located is a significant factor.
Managing the growth of these larger EV assets in urban areas, along with their centralized charging stations, presents a new problem for utilities to manage. But the deployment of these larger concentrated units is manageable, in part because they will require new electric service orders and an engineering analysis by distribution engineers. The broader looming problem is how do utilities model the organic growth of EVs behind-the-meter?
As we’ve seen with a number of leading technologies, early adopter trends tend to develop in geographical and socio-economic clusters. Whether that clustering is a function of regulatory mandates, utility incentives, commute patterns, availability of charging stations, or simply personal preference, greater numbers of EVs will concentrate in specific demographic regions. These regions will more than likely be in metro-suburban communities with shorter commutes and developed charging infrastructure.
Akin to the initial adoption of solar panels in suburban neighborhoods, excessive generation where there is insufficient load creates havoc on a distribution system. Conversely, the excessive loading on distribution transformers presents a far greater problem that could lead to equipment failures and outages. The distribution circuits in suburban neighborhoods are more than likely not designed to handle the swings in load created by simultaneously charging multiple EVs. Charging your EV on a hot summer night in a sleepy cul de sac could create a little tension in the neighborhood if your charger blows the transformer on your street.
Evaluating the cost of EVs
The potential burden that EVs pose on secondary voltage circuits could present serious operational and economic consequences. Reliability will continue to be job one for utilities and ensuring the fidelity of the grid will become even more complex as EVs, energy storage, solar and other DER permeate the system. This change in the system model – this evolution of the energy ecosystem, will require a hard look at cost of service models and a granular assessment of a DER’s contribution or expense to grid value on every circuit for every hour of the day.
In the case of EVs, multiple EV charging on that sleepy cul de sac could cause an outage that leads to an emergency response and transformer replacement, potentially costing rate payers $5,000 to $10,000 to replace equipment, not counting the cost associated with the outage. Even if the utility is proactive and upgrades a transformer prior to an outage, there is still a cost associated with providing that expanded capacity. How do utilities evaluate the growing load requirements and the costs associated with this expanded system capacity? Or do we take a page from the Non-Wire Alternative (NWA) play book and incentivize the behavior of downstream assets to manage capacity?
How do we model a mobile DER?
Modeling and integrating what can be defined as a mobile DER that functions as a load in one minute and a generator in the next, presents a complex problem that utilities must begin to address. Since we will never know at any given time where every EV is on the system, then the only logical way to model their performance is to model the charge points.
As outlined in the EEI report, the number of charge points in the U.S. will be close to 10 million by 2030, with nearly 80% of those located at a residence. Assuming these residential charge points are also served by an AMI network, modeling the hour-to-hour performance of these assets should be quite predictable. Understanding how these charge point assets are affecting the loading on a pad mount transformer in the cul sac will require a system of system approach, in which asset data from multiple DER systems, such as EV Charge Points, Distributed Energy Resource Management Systems, and ordinary premise meter AMI data, are modeled to forecast the aggregate real time and long-term loading on distribution transformers.
The systems of systems approach
Consider our cul de sac scenario again. Imagine several houses are served by a 10kW transformer and two residents are charging at a rate of 7kWh. If you do the simple addition, it’s easy to see that the math doesn’t work. On demand analysis can help utilities identify potential problems like this one. But the more difficult question is how do we address the issue before it manifests into an outage?
Regulation influences behavior
There’s a need for proactive policies to promote managed charging, smart charging and rate designs that encourage people to charge when it’s best for the system. EV manufacturers, including Audi, are interested in implementing demand response and time-of-use capabilities into their EVs. Automakers are interested in receiving demand response signals directly from the utility to help customers charge their vehicle while offsetting their utility bill.
Calls for demand-side flexibility should increase and with them the need to generate and communicate more near real-time data for purposes other than avoiding overloads. Fortunately, newly available technology now employs this “Ëœsystem of systems’ approach through a mature data virtualization layer that ingests and manages data from disparate systems and applies machine learning predictive algorithms across every DER, charge point or other measurement point. It forecasts the net generation or load at each asset and the aggregate loading on every transformer.
The system of systems approach allows utilities to model the sub-hourly performance at every meter and compare aggregate loading versus the nameplate ratings of the distribution transformer serving DERs, charge points and surrounding homes. Creating a grid that is more transactional can alleviate potentially disastrous situations like our cul de sac scenario. New variables impacting the power industry, like EVs, demand a new approach to a data-driven, cleaner energy, reliable and sustainable future.
Dan Garvey is a director at Powerrunner LLC. Dan has over 25 year of diverse energy industry experience beginning his career as an engineer with NStar, an energy manager with the US Navy, an account executive with Southern Company Energy Marketing and a technology sale executive with Siemens, Oracle (LODESTAR) and PowerRunner. From the power plant to the meter, his experience representing a retail energy supplier, a regulated utility and a large energy consumer provides Dan with a full perspective of the energy supply chain which has allowed him to build and deliver energy technology solutions that add business value to energy market participants.