Get Shift Done: The Power of Data to Accelerate the Energy Transition, On the Road and At Home

At Bidgely, we’ve coined a new phrase this year: Get Shift Done. In fact, we liked it so much, we put it on t-shirts. It’s a constant reminder that, among the many energy-business-enhancing applications for our data science, there is an urgent new priority to use our AI to accelerate the energy transition and the shift to beneficial electrification.

The transformation we’re all striving to achieve is a once-in-a-generation leap forward — both in terms of the utility business model as well as the future of the planet.?

When it comes to beneficial electrification, transportation and the cresting EV wave is often top of mind, with good reason. In less than 10 years, analysts predict 200 million chargers will be installed, accounting for 550 TWh of charging. But home and building electrification needs to be prioritized as an equally important part of the equation.? Energy use by residential and commercial buildings contributes approximately 27 percent of global CO2 emissions --– much of which stems from heating and cooling. Beyond just their decarbonization potential, both transportation and home and building electrification are being pursued with added urgency as a matter of national security and economic stability in the face of global conflict and gas price volatility.??

The revenue potential associated with beneficial electrification is also significant. From EVs alone, Boston Consulting Group estimates their electricity requirements 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.?

The reality is that getting shift done is an imperative in connection with virtually every future-ready energy goal.

But at the same time, as EV adoption grows and more appliances become electrified, increased grid congestion is almost certain, taxing transformers and grid capacity at levels they were not built to handle. Thankfully, with the dramatic increase in available energy use data, utilities are positioned to realize the beneficial electrification upside while mitigating the risks.

Whether on the road or at home, AMI data provides the amplifier utilities need to guide the adoption of new electrified technologies as well as the grid planning tools to successfully manage the dramatic new increases in load.

Paving the Way for Transportation Electrification

Data is enabling energy providers to prepare for and embrace the increasing pace and penetration of EV adoption.

As a starting point, AMI data analytics can detect EVs on the grid and reveal essential charging patterns and other EV insights from within each customer’s total raw home energy consumption profile. Our data scientists are able to identify charger types, charger amplitude, typical hours when EV charging happens, if charging is occurring on a schedule, and monthly EV consumption — all with a very low false-negative and false-positive coverage.

With these personalized EV analytics, energy providers are able to proactively engage with drivers as a trusted advisor that is uniquely qualified to help every driver optimize his or her EV-related energy usage. Instead of mass marketing, utilities can affordably and efficiently conduct personalized outreach to drivers about the impact of their charging habits on their energy costs, beneficial EV rate plans, charging programs that can improve efficiency and the charging equipment best suited to their household. This regular cadence of relevant engagement serves as an essential foundation of ongoing collaborative relationships with EV drivers that will prove essential for grid management moving forward.

Speaking of the grid, smart meter EV insights give energy provides an essential view into the total charging consumption and EV load by region, zip code, substation or feeder; the percentage of level 1 vs. level 2 chargers; EV load forecasts; percentage of on vs. off-peak charging; specific geographies with the highest charging; and more. This data can help utilities determine with high accuracy where grid constraints may exist or are likely to develop as a guide as to where and when to upgrade or install grid infrastructure.

In addition, sophisticated AMI data disaggregation? is able to identify those customers who habitually charge on-peak — making them a priority target for both behavioral and direct managed charging load balancing programs. For example, we’ve developed ongoing behavioral digital alerts or “nudges” that reflect each customer’s personal charging history in order to motivate them to change their behavior to charge off-peak. Sometimes these load shifting measures include an incentive that pays EV owners for charging their vehicle at optimal times. AMI data can then also be used to verify customers whether or not customers have changed their charging behavior.

Opt-in direct control programs in which vehicle charging can be managed remotely and optimized for the energy provider will be critical to maintain demand flexibility as EV penetration increases.? Energy providers can prepare for this transition by engaging EV owners with signals to stop charging to reduce load when the grid requires.?

Building a Solid Foundation for Electrified Homes

With AI-powered data analytics, utilities can also hyper-target their heat pump program outreach to prioritize those customer segments who can realize the greatest benefit from new electrified appliances, such as those with high-use heating and cooling habits and/or inefficient HVAC systems and water heaters.

For example, when it comes to determining which homes should be targeted for heating programs, understanding which homes have electric heating and which homes have gas or oil-based heating is an essential first step. Similarly, it’s essential to understand whether a home has a window, mini-split, portable or central air conditioning. Disaggregated household energy use data makes fuel and appliance identification easy.

In addition, since appliances often start consuming more energy as they approach end of life due to degradation, utilities can also run queries to identify changes in the duty cycle curve or other cycling patterns to identify inefficiencies.?

Building on that foundation of appliance-specific usage understanding, energy providers can then initiate one-to-one marketing outreach that highlights the most individually relevant benefits of heat pumps or electric water heaters and includes customized purchasing recommendations, such as those for rebates and installation professionals.?

Ultimately, motivating homeowners to take action requires connecting utility beneficial electrification programs with the individual needs and goals that matter most to each customer. Making the decision to electrify appliances is driven by a variety of personal factors, including a range of economic and sustainability motivations. Data-driven outreach empowers energy providers to more successfully capture customers’ attention and prompt action using personalized marketing that aligns with each customer’s needs, unique circumstances, and values.

Much like with EVs, AMI grid insights are also a powerful strategic grid planning input. Utilities are able to see the total appliance-specific load by region, zip code, substation, or feeder; the percentage of appliances with a given fuel type or technology; appliance load forecasts; percentage of on vs. off-peak usage; specific geographies with the highest appliance-specific usage; and more.?

Historically energy providers have only been able to evaluate energy usage at the substation level, or in some cases, at the feeder level. Now, by applying AI to smart meter data, utilities are empowered to understand load at the per-appliance-use level and more accurately predict future load patterns and determine what kinds of loads are available to shift.

For example, if heat pump ownership is 3% in aggregate across the territory, the utility might determine that heat pump load shifting will not play a valuable part in its non-wires solutions. But what if, in fact, the load-constrained geography ownership is actually >20 percent? In that case, shifting heat pump load should be a critical component of grid management.

With appliance-level insights, it is possible to identify feeder lines or transformers that are at risk of disruption and develop mitigation strategies before problems arise.

Appliance and transportation electrification requires a change in the customer-utility relationship in which customers become important utility partners who play a pivotal role in resiliency and the energy transition. Energy providers will no longer exclusively sell kilowatt hours in a one-way transaction to passive consumers. Instead, consumers now also have a role to play in managing supply and reducing stress on the grid during periods of high demand.

With more electrified appliances, smart home energy management systems (HEMs), virtual power plants (VPPs) and other connected technology solutions will facilitate utility-consumer load-balancing collaborations, enabling utilities to tap into heat pumps, grid-interactive water heaters, smart thermostats, managed electric vehicle charging and energy storage systems to reduce and reschedule energy use to improve grid reliability.

How Much Shift Are You Getting Done?

There is no question that AI-powered data insights make it easier to achieve transportation, home and building electrification goals on or ahead of schedule and get shift done.?

All energy providers have to do is to tap into the power of the home energy use data they are already collecting and put it to work to enable hyper-targeted and personalized customer engagement; more accurate electrification-related grid forecasting and more successful demand side management.?

Bidgely UtilityAI is excited to be on the leading edge of empowering smart energy decisions and action for transportation electrification and beneficial electrification.??