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Charging Technology

3 Key Challenges of Managed Charging for Electric Vehicles and How to Solve Them


November 11, 2021

SEPA has recently come up with a report on managed charging for electric vehicles. Amongst others, the report on how electric vehicles are being integrated into the electric grid and energy supply is especially relevant for workplace charging and home charging. However, the major underlying concerns from utilities are 1) the availability of EVs at the charging station (EVSE) to manage and 2) the uncertainty regarding the participation of the consumers in EV programs. Apart from that, the most crucial concerns are on 3) the cost-benefit uncertainty and 4) the limited experience in how to create a managed charging program. Here at ChargingLedger we have discussed the four concerns regarding how the future of managed charging will be and how effective tools can control the risks within the utility industry.

Four concerns regarding how the future of managed charger

1) Access and availability of electric vehicles for managed charging

Most hardware provider of charging stations either intend to add a communicating interface to a backend ecosystem or already have them in place. While OCPP is the major standard for this, quite a lot of manufacturers have created individual protocols specifically for their hardware products. This, can make it quite challenging but is not a herculean task to connect charging stations and management systems. As long as the charging interface is provided with an interface, the integration of managed charging is possible:

  • The protocol of the vendor can be integrated
  • The backend of the third party’s system can be connected
  • The integration to the utility’s system is possible if the utility already has access

2) The tentativeness of customer participation in the programs

TOU rates example

In the past, a record number of customers have willingly participated in several managed charging programs. For instance, the utility Green Mountain Power reported in their latest EV program, that just 2% of people have used the opt-out function when the charging cycle was shifted (or intended to be shifted soon).

However, most of these previous pilots involved managing charge indirectly according to  time-of-use rates or time-of-day rates (=no direct communication between a management system and the charging station). In the case of a system that is capable of sending a message directly to the charging station, we see significant benefits as compared to an indirect managed charging system.

In indirect charge management,  the energy rates are developed to allow the user to react on variant day prices. Direct communication to the charging station is missing. However, with directly controlled charging system:

  • Users do not need to react on prices when the charging station reacts
  • Users are not penalized with high costs when they do not abide by the TOU rates
  • The pattern of customer behavior helps to provide the best comfort at best prices

3) Cost-benefit Uncertainty

The cost benefits for the utility can be summed up in the four main categories:

a) Price Matching – Matching EV charging to the price of the energy generation or the wholesale energy price can give a clear benefit to utilities as well as to consumers. The EV charging usually takes place at peak times (6-9 PM) which drives the costs of the energy company and the commercial customer. Consequently, fixed rates for users will usually harm the utility's profitability. However, as the EV charging is mostly very flexible, utilities are in a better position to prevent peak-price charging with the application of managed charging.

b) Customer retention, new customer, and customer satisfaction – For utilities, EV users are quite attractive due to their higher energy consumption compare to non-EV users. Hence, it's even more important to notice that when avoiding any options of managed charging, utilities risk the loss of clients. The utility may also face a difficult time attracting new customers and achieving their respective annual sales targets. Moreover, utilities have to draft strategies to handle low customer satisfaction owing to high electricity bills for increased demand prices for their charging station. In contrast to this, when a state may not allow consumers to change supplier, a missing of managed charging lead to a reputation problem.

c) The risk associated with EVs – EVs pose a great risk for grid reliability and grid resilience. Certain studies suggest that EV penetration of above 6% will lead to 17% of the transformer or substation to be exchanged. This is time and cost intensive. These developments may require construction on the roads as well as waiting time for users even before installing a new charging station.

d) Missing use of renewable sources – When states, cities, or utilities reveal their target of providing a certain level of renewable energy by a specific period, these targets are needed to be achieved over time. (such as New York's Green New Deal reported by Bloomberg) The growing number of EVs can help these companies in achieving their targets. Nevertheless, if the EV is charged on a wrong time which means without considering the temperature, weather, and climate, it may become difficult to achieve the renewable goals. Having a concrete system to overcome these problems help achieve the target.

renewables electric vehicle charging

Limited guidance on how to craft a managed charging program

Managed charging is a vast practice that varies from state to state. Nevertheless, there are a good number of pilot programs that have successfully proven to be too good. Including but not limited to; how to integrate opt-out functions, incentives/or no-incentives for users, offering free charging stations for first-moves, or combining the programs with upfront rebates. The limited customer interaction, which is required, can be solved with simply using text messaging services or apps, which are directly communicating with the smart charging system. Moreover, it can be dramatically reduced by applying user patterns and machine learning algorithms in the optimization process of charging cycles.

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