In partnership with PLMA, this group is for practitioners from energy utilities, solution providers, and trade allies to share load management expertise and explore innovative approaches to program delivery, pricing constructs, and technology adoption.

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Load Management Potentials for EVs and Utilities

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writer and researcher BrightGreen PR

Julian Jackson is a writer whose interests encompass business and technology, cryptocurrencies, energy and the environment, as well as photography and film. His portfolio is here:...

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Big changes are happening on the grid, not least of which is the adoption of EVs, which will only increase in the years to come. However, there is no such thing as a free lunch and greater demands will be made on utility providers to cope with the increased usage of electricity.

If current projections are correct, estimates that electric vehicles could create between $3 and $10 billion in new value between now and 2030 for an average energy business with 2 to 3 million customers, according to Boston Consulting Group.

Utilities will need to adapt to take advantage of this dollar bonanza. This will need, in the first place, customer engagement so they know that their utility is a base of knowledge about EVs and charging. The goal is to educate consumers about all the information, customer support, preferential pricing, and programs that the energy provider has to offer to EV users, ensuring that the customer is primed to engage with their utility when they decide to move over to an EV type vehicle.

Utilities will need to use analytics to learn how EVs are being added to the locality and how much additional load they put onto the system. Of course they can also be mobile batteries and feed back when demand is high, but this is not so much of a challenge. Home smart meter data is considered to be much more reliable than either purchase data, which quickly dates, or vehicle telematics, which many owners choose not to share. EV owners should be incentivized to cease charging or sell back electricity to the grid at peak times, and utilize “off peak” power to charge their vehicles.

This information can assist utility companies to project with considerable accuracy where grid constraints may exist or are likely to develop. This will serve as a guide as to where and when to upgrade or install grid infrastructure. Similarly, data can illuminate areas of both existing and future high EV growth, which should identify prime locations for installing charging infrastructure.

By using this data, utilities should be able to predict where load will be highest, and enable systems to provide sufficient power. This will change considerably over a cycle of seven days, which is why advanced analytics software will be necessary to obtain the greatest insights.

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