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Our Electric Future - How electrification benefits customers and utilities – and how AI can help

There are few things in this complex, opinionated and messy world that can be categorized as universally positive. There’s a strong argument to be made that increased electrification is one.

Obviously, one of the primary beneficiaries of increased electrification would be the utilities supplying the electricity. The Electric Power Research Institute (EPRI) projects that efficiency gains will lead overall electric loads to decline in the absence of what it terms “efficient electrification” initiatives. But EPRI calculates that pursuing electrification would lead to cumulative load growth of between 24% and 52%.

But by no means would utilities be the only winners if electricity were more ambitiously harnessed to do everything from power cars, buses, heat pumps and warehouse equipment, or to grow crops in large indoor warehouses.

In short, a lot of good things would happen. Last April, EPRI released a National Electrification Assessment that outlined the societal, customer and utility impacts of electricity providing up to 50% of final energy consumption by 2050. The results: Increased grid efficiency and flexibility, improved human health thanks to better air quality, reduced energy consumption and consumer costs, as well as significantly lower greenhouse gas emissions, even in the absence of climate policy.

How does increased electrification deliver such wide-ranging benefits? The example of electric transportation is illustrative. Since 2000, the electric power sector has reduced its emissions of carbon dioxide by 20% and sliced criteria air pollutants by 80%. Which means that vehicles with batteries that are charged by the grid are becoming less damaging to air quality and the climate every year that the overall grid gets cleaner – which is every year.

But capitalizing on the promise and benefits of increased electrification requires new tools to help utilities robustly manage, optimize and ultimately monetize the opportunities that come from a modernized and transformed grid. Artificial intelligence (AI) is one of the most essential tools for utilities to embrace.

The use of AI to encourage and optimize the expected influx of electric vehicles provides a powerful example of how load-level information can be used to deliver benefits to EV drivers and utilities alike. Already, momentum around EV adoption is accelerating. According to Bloomberg New Energy Finance (BNEF), sales of EVs will increase from 1.1 million in 2017 to 11 million in 2025 and 30 million in 2030.

“A lot of that is being driven by the fact that EVs are becoming more and more affordable,” said Abhay Gupta, co-founder and chief executive officer at Bidgely. “Internal combustion engines will start to become more expensive than electric vehicles in short order.” In fact, BNEF research in 2018 found that lithium-ion battery pack costs averaged around $208 per kilowatt-hour in 2017. By 2030, predicts BNEF, the cost will have dropped to about $70 per kilowatt-hour. The same report projects that EVs will reach price parity with internal combustion powered cars by 2024.

AI can be used to accelerate the adoption of EVs and, once people have them, ensure that drivers and utilities are getting the most out of them financially. Here’s how it works: AI allows for energy disaggregation, which is simply a complex way of saying that it provides visibility into the fine details of the minute-by-minute energy usage of critical loads within a house. Visibility into EV charging – 80% of which takes place at home – provides information that can be leveraged to assist the homeowner, the utility and the grid as a whole.

One of the biggest problems is that the grid was never designed to have that high a capacity of demand at the same time. For example, if 10 homeowners on a street start charging their vehicles with fast charger at the same time, it is likely that we will surpass the capacity of the distribution transformer for that street. Every car manufacturer is about to release long range battery cars that even with fast charging would take 8-10 hours to charge overnight. This means that the option of staggering the car charging by a few hours would even not work for the grid as the adoption of EVs take on. The role of AI is critical at every stage. The first stage of growth is to identify which homes have EVs and help them move to time of use pricing. As the number and battery size of EVs increase - the role of AI may become arbitraging and forecasting who or how many homes in the street or on a transformer will charge when and combine with super dynamic pricing to offer super low pricing at times when there is excess capacity and super high pricing when you have higher demand. An example of this is Uber pricing - when demand is high, uber prices go up and users can decide to pay that price, or take alternate transport or wait for 30 minutes for demand to go down. This is just one proposed solution - there may be many other solutions - combining generation and battery storage with charging is another one that further complicates the equation of who can charge when.

For instance, AI can alert a utility that a home has an EV using a level one charger that typically begins charging a battery at 6 p.m. each night. If you know that as a utility, you now have the option to encourage that user to put a timer on their charging so they can charge at midnight when electricity is cheaper instead of 6 p.m. Or, you can even offer them a level two charger at a discount, which allows the utility to have control of the charger. Then the utility can send price signals or maybe even control that charger to help manage grid load.

These are all options that are financially advantageous to both utilities and EV owners, but they are options that utilities can’t confidently present to customers without the sort of granular information that AI provides. Utilizing AI-enabled visibility to offer EV-friendly rates or discounts on chargers helps utilities economically manage what can often be costly peak demand.

But it also is a way to ensure that utilities are relevant and resonant in their communication with customers – it’s the kind of data that utilities need to help foster everything from interest in EVs to participation in utility demand side management and energy efficiency programs.

By taking this information, you’re able to have targeted offers that make sense for people. It’s not just an offer. You become that trusted energy advisor that utilities want to be.

Abhay Gupta's picture

Thank Abhay for the Post!

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Dr. Amal Khashab's picture
Dr. Amal Khashab on Oct 30, 2019

Looks that AI makes EVs is a complex problem. Utilities have to find solutions out of the box. Business model of separate micro-grid dedicated for EV is one . It can be expandabe and use distributed RE sources as much as can.

Sophia Wen's picture
Sophia Wen on Oct 31, 2019

AI can have a specific role to play. Utilities are often looking at AI solutions because they can help with the decision making of investing in infrastructure like a micro-grid. They can use AI to forecast, understand, and potentially control EV charging so they can determine at what point (accounting for cost & capacity) a dedicated grid may be necessary.

Matt Chester's picture
Matt Chester on Oct 30, 2019

How does increased electrification deliver such wide-ranging benefits? The example of electric transportation is illustrative. Since 2000, the electric power sector has reduced its emissions of carbon dioxide by 20% and sliced criteria air pollutants by 80%. Which means that vehicles with batteries that are charged by the grid are becoming less damaging to air quality and the climate every year that the overall grid gets cleaner – which is every year.

So happy that the energy transition has progressed that we can finally put to bed the counterpoint to EVs that they are worse than ICE cars because of such a dirty grid. Electrify everything then decarbonize electricity: it's a strategy that's quickly gaining steam!

Sophia Wen's picture
Sophia Wen on Oct 31, 2019

Couldn't agree more Matt! 

Rex Berglund's picture
Rex Berglund on Oct 30, 2019

EPRI's estimated load growth is similar to NREL's. The midpoint of the range 24% to 52% is 38%, just what the National Renewable Energy Laboratory’s Electrification Futures Study series gives as an estimated increase in electric use by 2050 in their high scenario.

Fortunately for the adoption of renewables, LBNL has modeled the use of EVs instead of stationary batteries, finding that for the example of California it could save billions:

Let’s sum up the findings from the paper on how the expected number of California EVs can help to ensure grid stability and fulfill the intent of the storage mandate:

Without hindering drivers’ transportation needs, smart charging or V1G can easily provide 1 GW of storage, or about three-quarters of the 2024 storage mandate.

V1G and V2G combined can offer an astounding 5 GW of storage, dwarfing the storage mandate, and enabling the integration of much higher quantities of renewable energy.

Crucially, while V1G may require a system-wide investment of ~$150 million, that’s substantially less than the $1.45-$1.75 billion that equivalent stationary (non-EV) storage would cost. (The paper used stationary storage costs from 2015, the latest available at the time of its writing, but even with the substantially lower storage costs of today, V1G implementation remains far cheaper.)

Using a similar approach, the value of grid services associated with V2G in addressing the “duck curve” is equivalent to $12.8 to $15.4 billion in equivalent stationary storage.



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