According to the International Energy Agency, EV sales jumped from ~1 million to more than 10 million over the course of the last 5 years, including an increase of 55 percent from 2021 to 2022.
The increasing pace of transportation electrification is making managed charging an essential strategy to maintain grid resiliency. Unless we coordinate who is charging on a substation or a transformer at what time, we risk a breakdown in reliability as well as over-investment in grid infrastructure and the financial burden that would place on all customers.
“We already have 70,000 electric vehicles on the road in Washington State. And our governor has put a stake in the ground and said that there’s going to be a lot more. In fact, every new vehicle sold will need to be electric, hydrogen-fueled or hybrid with at least 50 miles of electric-only range by 2035,” says Heather Mulligan, Manager of Customer Clean Energy Solutions at Puget Sound Energy. “So we need to start thinking now about that impact on the grid and how we manage for that.”
Adam Grant , Director of Electrification & Energy Services at NV Energy emphasizes that “It’s not that EVs are coming, they’re here. And the expansion is going to happen very quickly. Because it is going to be so prevalent and affect our capacity, we want to put forth a managed charging program eventually that allows us to have a little control over when the customer charges, in part to help avoid or defer upgrades to the system to meet the demand that we know is coming. And we’ll be able to build and make sure that we are and we will be ready for it when it all comes at full speed.”
It’s important to note that not every EV driver needs to be enrolled in an active managed charging program. Instead, the most cost effective and successful programs identify those customers who will have the biggest load shift impact, either based on their usage or their location. Bidgely helps utilities accurately identify and engage these ideal target customers by applying advanced data science and AI to AMI data to reveal residential charging patterns.
For other EV drivers, demand response or “passive” managed charging programs that encourage behavior modification without direct controls can provide a secondary means to manage EV load. Bidgely’s behavioral expertise provides an invaluable tool.
In this scenario, AI-powered insights derived from AMI data enable utilities to send customers behavioral nudges through email and SMS whether or not they have enrolled in an EV program or allowed the utility to connect to their vehicle equipment. Passive managed charging can be particularly useful in support of a Time of Use program to ensure customers benefit from the rate structure.
“We know these EVs are coming, and they are going to be a demand on our system. Our biggest challenge is integrating that load on both a volume basis and on a timing basis,” says Charles Spence, Manager of Vehicle-Grid Integration Programs at Avangrid. “We have end-of–day peaks already. When people come home, they turn on their AC, the TV — all of these appliances. And now, when they plug in that EV too, it will create an issue where it’s not just the volume, it’s the time. That is what we’re trying to do with our demand response managed charging programs.”
In either case, both passive and managed charging require a new level of utility-customer partnership.
Grant says, “Overall, managed charging is a big opportunity for a utility to work with customers to support the grid.”
Utilities have realized that achieving favorable electrification outcomes requires working collaboratively with customers to help them charge at the right place and right time. The goal is to realize a mutually beneficial scenario where customers are always able to get the charge they need when and where they need it, while also doing their part to ensure the resiliency of the grid to the benefit of the larger energy community.
“I would say that in the future 25 percent of our energy capacity is going to come from the customer, and 75 percent of it will come from energy generation. And so it’s really important for us to understand and unlock that 25 percent that is in the distribution system so that we can meet our decarbonization targets,” says Larry Bekkedahl, Senior VP of Advanced Energy Delivery at Portland General Electric. “To do that, we need to know when you’re charging, how you’re charging, and, oh, by the way, do you even have an EV in your garage? If I know, and work with you, and ask you if tomorrow, from 4 to 7 p.m., if you could reduce some of your load, the customer benefits and we benefit. They’re willing to work with us.”
Data-driven managed charging programs require sophisticated disaggregation capabilities. EV signals overlap with many other appliances, requiring powerful AI for accurate EV identification.
Bidgely possesses an EV knowledge base that consists of advanced ground truth for geographies in both North America and internationally that other technology providers cannot match. Our data set allows Bidgely to pinpoint who has an EV and their monthly consumption, charger size and typical hours of charging with high confidence -– even in traditionally hard-to-detect cases. All of this intelligence is made possible without any hardware or customer inputs required.
This advanced data science foundation is empowering utilities to develop highly targeted EV managed charging programs that more successfully and economically engage EV drivers as grid resiliency partners.
As I point out in the video below, its important to identify and target those customers that will have the biggest impact when it comes time to shift that load and support the community.
This approach not only delivers greater load-shift outcomes, but also allows utilities to create 3X more cost-effective programs – shifting more load for less dollars.
To learn more about Bidgely’s EV Solution and managed charging expertise, download our Electric Vehicle Adoption Playbook.
To see a walk through of the Managed Charging Customer Journey, access our demo portal.
However, the challenge lies in isolating the right data that truly aids decision-making, while recognizing that more data doesn’t necessarily equal better data or lead to better outcomes. Utility decision-makers require access to more meaningful datasets that are designed to quickly surface relevant and actionable insights that will deliver improved results for their grid, their customer relations and their bottom-lines.
Bidgely’s next generation business intelligence platform streamlines the process of gathering relevant insights about your customers and your grid so you can achieve better outcomes. This includes being able to more confidently plan grid optimization that accounts for DER adoption and demand-side shocks, and uncovering untapped opportunities for load management.
Utility users can now use the platform to analyze end-use demand data and understand which appliances customers are using at every hour in the day. This data can be used to answer complex questions such as “how do customers respond to weather shocks compared to prolonged extreme weather and in these two scenarios which appliances are they turning on and when.”
An essential tool in a utility user’s toolbox, is an 8760 demand curve. An 8760 curve plots energy usage for every hour of every day for the entire year (24 hours x 365 days = 8760 points). These curves are used to analyze customers’ yearly consumption patterns and manage peak demand in the coming years.
Analytics Workbench 2.0 provides utility users with two 8760 curves that help them understand true customer behavior and grid capacity impacts.
The first 8760 curve visualizes total consumption over the year so users can analyze seasonal consumption trends and anomalies. Users can quickly review system-wide 8760 demand curves as well the 8760 for each transformer, feeder, substation, etc. This empowers utilities with the information they need to know what is happening on their grid at all times.
The second 8760 reflects end-use demand for every hour in the day. These end-use categories include EVs, heating, cooling, pool pumps, refrigeration, etc. Understanding how customers use energy at every hour in a day across the year provides utility users with a wealth of knowledge. Decision makers can quickly reference this hour-by-hour appliance curve to answer questions such as:
Utility decision makers face the challenge of answering complex questions and acting upon their conclusions with limited margin for error. Thus, it is crucial for them to have access to the right datasets, enabling them to ask the right questions, conduct meaningful analyses, and make informed decisions that drive tangible benefits to the grid and to their customers.
Analytics Workbench is specifically designed to provide decision makers with the necessary data and information required to effectively manage today’s energy transformation and anticipate the challenges of tomorrow.
These three trends ultimately result in the need for a much greater connection between customers and the grid, particularly to enable demand flexibility. In the past, flexibility primarily came from the supply side, as the demand side was seen as largely inflexible. However, the aforementioned trends have revealed two critical issues with the traditional approach:
Given these changes, as an industry, we need to shift from the practice of flexible supply to flexible demand. Customers and data are crucial parts of the flex demand equation.
As we approached this problem at Bidgely, we posed the question:
“how might we enable utilities to realize flexible demand with the data they already have?”
A variety of ideas emerged, and ultimately 3 innovations bubbled to the top:
In this blog post, we will dive into why DER Grid Planning needs to enable demand flexibility through bottom-up data visualization.
As we explored the challenges associated with demand flexibility, one key aspect stood out: the necessity of a data-driven approach to facilitate analysis of non-wires alternatives. The potential for significant reductions in both bulk load and localized demand is huge. RMI notes that:
“Examining just two residential appliances—air conditioning and domestic water heating—shows that ~8% of U.S. peak demand could be reduced while maintaining comfort and service quality.” [1]
However, many utilities are blind to where these flexible loads actually sit on their system and therefore they cannot appropriately plan for NWAs or even right-sized grid deployments.
This issue of visibility into the system led us to think about how to combine behind-the-meter (BTM) analytics in the form of disaggregation to grid planning and NWA analysis.
In order to provide the visibility needed we took all of the household meters on a particular circuit and performed disaggregation on each household to reveal what appliances were at the household and when the specific load was utilized.
By aggregating the 8760 load curves, along with end-use designations, from all households within the circuit, we obtained the overall load curve for that circuit. This approach allows users to pinpoint the hours of constrained grid operation over the past year and identify the potential load that can be shifted during those periods. The goal is to be able to identify if there is enough flexible load available during those constrained hours to avoid the need for costly system upgrades. While not every circuit may have the potential for non-wires alternatives, prioritizing circuits with the highest potential is essential.
To meet the needs of their customers and integrate renewable energy sources into the grid, electric utilities must prioritize demand flexibility. This requires investing in a flexible grid infrastructure, accurate demand analysis tools, and mechanisms for incentivizing customers to shift their energy usage patterns. By doing so, utilities can provide reliable, affordable, and sustainable energy to their customers.
[1] RMI’s The Economics of Demand Flexibility report
And while renewable energy and beneficial electrification are an essential part of the solution, smarter energy usage in the form of energy efficiency, demand response and other demand side management (DSM) programs are just as integral to achieving decarbonization.
In fact, while adding more clean energy to the grid and electrifying all vehicles will take decades, next generation DSM can make substantial progress today. Utilities – and society – need the full range of DSM programs to achieve meaningful net zero progress now while we continue to transition our energy supply, electrify transportation, and update essential infrastructure.
Energy efficiency programs are by far the largest DSM effort, and the energy savings accrued from improvements in energy efficiency is the simplest and least expensive path to reducing greenhouse gas emissions. Engaging consumers through next-generation energy efficiency programs delivers immediate carbon impact with lower start-up thresholds.
Utilities worldwide turn to Bidgely to facilitate their DSM program evolutions, upgrading existing programs to deliver more timely, personalized, and relevant energy experiences to every customer, which in turn improve energy efficiency savings realization rates, boost customer satisfaction and increase program/service uptake.
In fact, in 2023, Bidgely’s global customer base of electric utilities and energy retailers surpassed one terawatt-hour (TWh) of energy savings through Bidgely’s UtilityAI energy efficiency programs, which has offset nearly 709,000 metric tons of CO2 emissions. Those gains continue to accelerate. By 2028, Bidgely and our utility partners will have more than doubled energy efficiency returns, achieving an anticipated 2.6 TWh of savings.
Together, we’re achieving emission offsets that would require thousands of solar panels, hundreds of wind turbines, millions of dollars and many years of project development by instead enabling smarter usage and deployment of the grid today. And it’s all made possible through the power of data.
“For years we have expected the energy transition to be built largely with increased renewable energy production. The reality is our greatest weapon rests with smarter energy,” said Jen Szaro, President and CEO, AESP (Association of Energy Services Professionals). “Bidgely’s milestone is proof that having detailed analysis of real-time usage directly leads to smarter consumption and deployment, creating a cleaner environment for all.”
Every customer has their own unique DSM value. Understanding how to optimize each customer’s maximum savings potential is the key to achieving targets. Bidgely’s Analytics Workbench tool provides utilities with an in-depth understanding of every household’s unique load fingerprint -– i.e. what appliances are in use, the size and time of their consumption and/or demand, and how customers should be best targeted and incentivized to take savings actions.
Applying AI-powered analytics to meter data empowers utilities to target each customer with programs best suited to achieve behavior change to achieve the energy savings that the grid needs. Using these insights, utilities can meet customers where they are by providing personalized messaging based on that customer’s needs — treating customers as individuals, with individual energy savings and load shift opportunities.
For example, Analytics Workbench reveals whether a customer has an inefficient appliance, such as an AC unit that should be repaired or replaced, or an appliance that is healthy but being used inefficiently, and a smart thermostat could assist. Similarly, Analytics Workbench pinpoints pool pump usage, including both time of use and speed of the pump, enabling utilities to target specific customers with programs to replace single speed units with more efficient variable speed models, and/or run the pump in off-peak hours.
Maximizing efficiency savings also requires extending efficiency programs to all customers – not only those in the highest consuming homes. Using Bidgely’s patented disaggregation technology, Analytics Workbench identifies variables beyond high consumption to create superior treatment groups with the highest propensity to save. Maximum savings is determined by a combination of consumption, appliance usage, energy lifestyle, and behaviors — a customer profile that Bidgely’s advanced data science is better able to define.
The difference between legacy DSM approaches and Bidgely’s AI-informed practical, simple and personally relevant efficiency recommendation approach is significant, with an average 3% energy savings per household. With millions of households, this adds up.
Insights informed by granular household energy use data can empower utilities to make essential and immediate progress toward their ambitious energy efficiency goals.
As Krystal Maxwell, Research Director, Guidehouse asserts, “Machine learning, AI, and smart meter data enable highly targeted, strategic implementation of energy efficiency programs. This approach provides distributed energy resources (DER) integration, grid optimization, and increased energy efficiency to provide relief to a utility’s energy grid, while simultaneously providing bill savings to utility customers.”
The proof of the power that lies in Bidgely’s advanced data science, is in the results industry leaders are seeing today.
Rocky Mountain Power, a Berkshire Hathaway Energy Company, achieved over 228 GWh energy savings at a cost savings of 25 percent compared to traditional energy efficiency programs by partnering with Bidgely.
Vice President, Customer Experience & Innovation for Rocky Mountain Power William Comeau commented, “The transition to a sustainable future is a partnership with our customers, and it’s data that helps us target offerings specifically to them from an energy efficiency standpoint. Having that partnership with our customers helps them reduce their load during peak times, helps us keep costs low overall for our customers, and long term, bring on more sustainable solutions.”
Adam Grant, Director of Electrification & Energy Services for NV Energy, another Bidgely partner that yielded 13 GWh energy savings in the first year and 40 GWh in its first three years of its energy efficiency program, reflected, “We work extremely hard to be partners with our customers; to teach them ways to use and ways to save energy. Because of the data and targeted aspect of what we’re doing, this is not blanket marketing: we take what we know about precisely where and why customers were inefficient and help them become more efficient.”
If you’re interested in maximizing the contribution your DSM programs make toward achieving your energy efficiency and decarbonization goals, learn more about how Analytics Workbench and Bidgely’s intelligent, next-generation energy efficiency programs can deliver measurable results today while also helping you build the grid of the future.
As consumers, we constantly receive targeted ads in our inboxes, social media platforms, search results and more that offer suggestions as to what we should watch, listen to, buy or attend. Those ads are typically very relevant to us and in line with our preferences, because advertisers know that this behavioral science based approach makes it more likely that we will 1) pay attention and 2) respond to their call to action.
This type of data-driven personalized outreach has transformed every aspect of the consumer marketplace. We’ve all come to expect that companies understand our wants and needs and will meet us where we are with tailored recommendations, and their energy providers are not immune to this expectation.
Recognizing that this mindset is driving consumer behavior is particularly important as the utility industry increasingly needs the partnership of their consumers to participate in peak events and otherwise help alleviate peaks on the grid. Today, behavioral demand response programs must be framed around data-driven personalization, especially when it comes to calling peak events. Meeting customers where they are with an understanding of their energy use needs and habits will translate into messaging that is more impactful and, by extension, ensure greater peak event participation.
For example, when extreme heat impacted much of the United States and the world during the summer and fall of 2022, utilities and government officials in California, Texas and elsewhere not only broke generation records, they also made headlines for asking their customers to curtail their usage to prevent outages. Their peak event outreach campaigns included mass text alerts, emails, and social media posts asking every customer to conserve. In essence, the messages said “we’re all using a lot of energy. Please use less.”
The good news is that, for the most part, it worked. Over the course of a few days, customers made what amounted to educated guesses as to how they could reduce their load, and utilities were able to avert mass blackouts.
But as extreme weather becomes more common and beneficial electrification continues to grow, consumers are going to be called upon to participate in peak events much more frequently and for longer periods. As those peak events become a more regular occurrence, blanket calls for non-specific actions will be less effective.
Without personalization and relevance, utilities risk creating a sense of “peak event fatigue” in which consumers become less interested in participating over time because calls to action are vague and don’t show consumers what behaviors they need to change.
Engaging consumers in the sorts of numbers that will be required to ensure grid resiliency without direct load control requires precise requests for specific and relevant actions. Utilities need to educate each customer about their energy use on a per-appliance and day-and-time-of-use basis so that they understand exactly how they can make a meaningful contribution to reducing grid load. It comes down to targeted messages with recommendations about specific behaviors sent at the right time to the right person.
When it comes to providing consumers with energy insights that drive peak event participation, there are a variety of tools that can make a meaningful impact.
For example, when it comes to utility outreach, the goal is to evolve messaging from non-specific asks like “turn off or reduce nonessential power” to hyper-personalized and data-driven recommendations like “70% of your energy use is attributed to cooling during afternoon peak periods. Adjusting your air conditioning use during tomorrow’s peak event will help the grid. Pre-cool your home between 11 a.m. and 1 p.m. and then set the thermostat to 78 degrees until 6 p.m.”
On the self-service front, energy use activity maps allow customers to research their load profiles and learn which of their appliances use the most energy, and what days and times of day they use the most energy. With this personal level of detail, consumers are empowered with what choices they have at their disposal to make the greatest impact on the shift of their overall load. In the context of peak events in particular, an activity map empowers consumers to identify which of their energy use behaviors typically occur during the peak event time, and how they can change those behaviors to do their part to ensure grid resiliency. If the peak event is from 4 to 6 p.m. that day, and they typically charge their electric vehicle during that time, they know they can make a difference by shifting their charging to overnight instead of post-commute.
The guiding principle for all personalized outreach is that coaching consumers to change specific behaviors to outside the peak window will yield greater grid gains.
Establishing the one-to-one consumer understanding that serves as the foundation for this personalized approach requires sophisticated machine learning and statistical solutions to analyze raw energy consumption AMI data. Bidgely’s advanced Time of Use (TOU) disaggregation breaks down household consumption to previously indiscernible granularity. This breakthrough level of precision opens up a new world of customer engagement possibilities for energy providers.
Granular energy use insights allow every step of a customer’s energy use journey to be both hyper-personalized with a clear explanation of the benefits they’ll realize for taking specific action based on how they use energy. Optimizing customers for success in this way is not only essential for peak event participation, but also to more effectively guide customers through billing, time-based rates, ongoing energy efficiency and more.
Learn more about how Bidgely’s Flex Demand and Analytics Workbench solutions empowers utilities and their customers with relevant, actionable energy insights to more effectively engage consumers in managing peak load.
This urgent need to shift load is one of the major factors driving the transition from flat, volumetric rates to time-based residential rate structures that incentivize consumers to align their energy use with times of greatest supply and lowest cost generation.
When executed well, time-of-use (TOU) rates improve system utilization, reduce peak demand, and boost customer experience (CX) by empowering consumers to take charge of their energy costs and save money.
Though the promise is great, at the same time, the transition to time-based rates is complicated.
Consumers have paid a flat rate per kilowatt hour for electricity for decades. Changing to new rate structures can inadvertently lead to confusion and bill increases — which undermine CX and consumer trust in the utility.
With so much at stake, future-ready utilities are leveraging household energy use data analytics to inform their time-based rate design, rollouts, and ongoing management to avoid potential pitfalls and realize greater grid and CX benefits.
In an era of accelerating beneficial electrification, electric vehicle TOU rates are one of the most compelling emerging time-based rate use cases.
For many electric vehicle drivers, the ability to save money on fuel was one of the most significant drivers in their decision to buy an EV.
EV TOU rates provide EV owners with a way to amplify their fuel savings by charging their vehicles during off-peak hours when energy is the least expensive. In this way, utilities are in a position to offer EV-driving customers TOU rates that maximize fuel savings, which can translate into a big CX boost. And, at the same time, enthusiastic driver participation in EV TOU rates enables utilities to effectively shift load to avoid EV-related grid strain.
Bidgely’s advanced AI technology and AMI analytics provide utilities with the customer-specific vehicle insights they need to personalize each consumer’s TOU rate journey, making it a positive experience from initial enrollment through long-term participation:
1) Leverage Disaggregation as a Strategic EV TOU Input
As with any TOU rate, AI-powered disaggregation detection and insights should be the foundation for time-based EV rate design.
Bidgely’s smart meter energy intelligence reveals which customers have purchased an EV, when they are charging their vehicle, and what equipment they’re using to do so.
Leveraging these insights enables essential granularity of customer segmentation and price-differentiated periods, with rate structures tailored to a wider variety of EV-driving customers with distinct demand profiles.
2) Personalize Recruiting and Onboarding
As a fundamental customer engagement strategy, Bidgely’s EV Solution provides EV drivers with personalized monthly summaries of their EV charging habits and spending, including personalized tips to optimize their charging.
This ongoing collaborative engagement serves as an ideal time to promote TOU rate plans to those EV users who are not already enrolled.
Promoting and recommending the adoption of EV TOU rates requires that customers 1) understand the mechanics of a TOU rate and how it works; 2) understand how, based on their unique historical load and consumption habits, they could save money compared with their current rate; and 3) understand whether achieving the forecasted savings will require changes in current EV charging behaviors. Customers are more likely to opt into a time-varying EV rate plan when the advantages of doing so are explained in specific and relevant terms – with personalized targeting rather than mass marketing — including calls to action that are customized with personalized savings potential based on each customer’s vehicle, charging equipment, and charging requirements.
Customers who receive this type of rate education are most likely to want to participate in TOU rates and remain engaged, ultimately contributing more significant peak-time reductions immediately and over time.
3) Provide Ongoing Personalized Coaching
The more EV customers embrace time-varying rates, the greater the gains in grid management, costs to serve, customer savings, and customer experience outcomes. But as with any new experience, EV drivers need coaching to make sure they are using their EV TOU rate effectively to achieve maximum load shifting and savings. As with program recruitment, one-on-one engagement makes coaching more effective.
Bidgely’s EV solution includes a wide range of continuous coaching tools, such as monthly bill projections, budget alerts, and weekly reports to track usage trends and avoid high bill surprises.
In addition, Bidgely’s peak alerts provide EV drivers with immediate corrective feedback on negative charging behavior, letting customers know in the moment exactly how much more they paid for the on-peak charge versus an off-peak charge. These specific dollar figures are useful to both prevent high bills as well as encourage the customer to shift charging in the future.
Bidgely’s self-service TOU rate research tools provide another essential means for customers to better understand how to optimize their EV charging behaviors. Energy activity maps provide easy-to-interpret graphs that track energy usage during peak and off-peak hours by time of use and duration. This visual representation of energy usage is a powerful way to build customer understanding as to how their charging habits equate to energy costs, and what steps they can take to achieve the greatest TOU benefit.
Precise, accurate, and timely engagement empowers drivers to optimize their TOU plan fully and derive the most advantage, while simultaneously delivering the greatest grid benefit.
4) Evaluate and Optimize
New rate designs can sometimes have unintended outcomes, highlighting the importance of real-time monitoring of their impact.
Conducting AMI-data-based analyses of existing EV rate plan outcomes can serve as an essential tool to understand how customers are responding to the new prices and to evaluate the need to change or modify EV rate designs.
Understanding how user behavior is changing compared to a control group or even compared to a customer’s baseline can build a powerful understanding of what strategies are working.
Analyzing EV charging patterns on an ongoing basis enables utilities to responsively refine rate design to maximize energy savings for both the utility and the customer.
The promise of TOU rates to improve system utilization, reduce peak demand, and lower consumer costs is significant, but the transition necessary to achieve and continue to realize that promise is complex and requires a solid data foundation.
Bidgely’s patented data science for electric vehicles and other appliance-level grid insights enables personalized customer engagement and understanding that improves the long-term success of TOU rate programs.
To help utilities maximize the value of their AMI data to achieve TOU transitions, we’ve developed a Leveraging AMI Data for Successful Time-Based Rate Implementation playbook that outlines how to put energy intelligence to work to improve new rate structure development and implementation, including:
To explore more best practices for EV TOU and other time-based rate implementation, download your copy today.
That can be a daunting prospect for an IT organization — both in terms of the switching costs as well as the timeline required for a multi-system migration.
Utility IT teams have already invested money, time, and resources in CIS systems, customer care and billing systems, CRM systems, digital self-service systems, and potentially even legacy customer experience solutions. Understandably, the idea of throwing all of that out and starting from scratch creates a significant barrier to entry.
By contrast, Bidgely’s perspective is that there’s no need to upend your organization in order to upgrade your CX platform.
If existing systems are performing well, there is no reason to replace them. Instead, the focus should be on supercharging existing platform capabilities and elevating the value of the entire IT infrastructure to get the most out of your investment.
Behind-the-meter energy intelligence derived from load disaggregation and non-intrusive load monitoring has the capacity to augment existing tech stacks to maximize performance. In essence, consumer energy use data becomes a single source of truth that informs and boosts the capabilities of technology platforms across utility operational areas. Household energy use data serves as the core building block for both the CX platform and the aggregate IT infrastructure.
Take CRMs for example. Call center agents answering calls from consumers typically have very little knowledge about what’s happening in a caller’s home. Instead, every call requires a discovery process.
Leveraging AI-based disaggregation technology, Bidgely is able to inject appliance-level insights about a customer’s usage into the call center system, equipping agents to quickly understand why the consumer might be calling, even before picking up the line. This energy intelligence leads to faster call resolution, reduced hold times, and more personalized customer experiences.
Beyond customer-facing technology applications, a smart meter data-informed CX platform can also better inform grid management and planning by making it possible for utilities to better understand what the load on the grid is today and is likely to be tomorrow. This intelligence is crucial to achieving transportation, home, and building electrification as well as distributed energy resource goals.
Rather than introduce new technology systems, Bidgely’s CX Platform is a highly flexible solution that uses APIs and widgets to seamlessly infuse customer intelligence into the existing IT systems utility teams know well.
For example, our REST API Integration enhances a utility’s native mobile app with personalized energy insights. Our API Notification Integration enables a utility to send personalized insights via both email and SMS. Our widgets allow a utility to embed new CX features within existing web portals and call center systems to maximize customer personalization and relevance.
Seeking just this sort of flexible integration, Electric Ireland partnered with Bidgely to harness insights from its residential customer data to improve engagement across its existing digital channels. Bidgely’s insight tools were delivered as widgets that could be seamlessly integrated to improve email communications with tailored, customer-specific content and add personalized energy insights, individualized customer energy usage education, and recommendations to the energy retailer’s existing residential web portal. Nearly 33,000 customers accessed Bidgely widgets on the Electric Ireland residential portal during their first three months of availability, and Bidgely-informed email communications realized a 94.62% click-through rate, demonstrating customer enthusiasm for the deeper intelligence Electric Ireland was able to provide through its established channels.
The utility business model is changing rapidly, and it’s essential that a CX platform is built to evolve in response to emerging expectations from digitally centric customers and the transformative impact beneficial electrification, distributed energy resources, and other new technologies are having on grid operations. As utility needs and goals evolve, so too must their CX platform. Rather than implementing a CX platform for today, utilities need a CX platform that is always primed to deliver for tomorrow.
Unlike many static point solutions, Bidgely’s CX platform was designed to be dynamic. Customer energy intelligence continues to be enriched over time to reflect evolving energy use habits, preferences, and values. In this way, a utility’s tech stack is always informed by the most relevant, actionable data, and IT system ROI is continuously maximized.
For example, as beneficial electrification scales, managing load to maintain grid resiliency will become increasingly mission-critical for energy providers worldwide. The accelerating adoption of EVs, electric appliances, and DERs is creating a new utility-consumer dynamic in which consumers play a more active role in reducing stress on the grid during periods of high demand through direct managed charging, virtual power plants, and more. To accommodate this shift in business model, utilities will need to leverage smart meter data insights to deepen customer engagement. A CX platform capable of enhancing organization-wide IT systems with customer intelligence will be crucial.
Getting Started
In a crowded industry, there are hundreds of CX solutions vying for utility attention and promising to deliver the best results. The best platforms will deliver meaningful value by boosting the tech stack rather than disrupting it.
To help utilities evaluate their options, we’ve developed a Building a CX Platform Playbook with 10 practical criteria utilities can use to inform CX platform comparison, including those presented in this blog and more:
But reducing building emissions is an equally and increasingly critical part of the solution. In fact, energy use by homes and buildings contributes approximately 27 percent of global CO2 emissions – much of which stems from heating and cooling.
Without question, home and building decarbonization is foundational to achieving emissions goals. Not only is it a critical element of the carbon reduction equation, but the gas prices and political volatility have also elevated home and building electrification to a matter of global security and economic stability.
Many European countries are pivoting away from Russian energy imports and seeking non-fossil-fuel-powered cooling and space and water heating alternatives. In the United States, government officials used the Defense Production Act to ramp up heat pump production in the saying that over-reliance on fossil fuels leaves the United States and its allies vulnerable to threats and price shocks. Further, the U.S. Inflation Reduction Act included 10 years of tax credits and other incentives designed to make clean energy technology more accessible, including reducing the cost of heat pumps and electric water heaters.
But even as world leaders and energy providers increasingly prioritize home and building electrification, the majority of consumers aren’t yet on board.
The first barrier is that heat pump technology is not well understood — especially in terms of its viability for a wider-range of customers. Second, there is little consumer appetite to replace working HVAC or water heaters until their existing fossil fuel systems reach their end of life.
Globally, energy providers are in various stages of developing, getting approval and enacting beneficial electrification plans. Their progress is heavily influenced by customer expectations, fluctuating regulations, and their roll out of AMI infrastructure, meter data management systems (MDMS), generation management systems (GMS), demand response management systems (DRMS) and other technologies that facilitate beneficial electrification progress.
But no matter where they are in the process, there is a common understanding that customer engagement is perhaps the most important lever to achieving their goals. Engagement in the form of data-driven personalization, alignment and motivation is what it takes to inspire consumers to take action.
Leveraging Bidgely’s patented disaggregation technology, utilities can look behind the meter to each household’s appliance-level energy use. Bidgely’s Analytics Workbench business intelligence platform delivers energy intelligence that enables utilities to rapidly run queries on appliance ownership, time of usage and appliance efficiency; and filter those queries by date range, appliance type, rate class, geography, premise type and customer communication preferences.
Not only does this hyper-targeting make customer engagement by location, appliance ownership or time of usage possible, but also by type of observed behavior — such as higher-than-average cooling, heating or hot water usage.
For example, when it comes to determining which homes should be targeted for heat pump programs, understanding which homes have electric heating and which homes have gas or oil-based heating is an essential first step. Similarly, it’s important to understand whether a household is currently operating window, mini-split, portable or central air conditioning. Then, 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 and optimal targets for replacement programs.
Utilities can leverage appliance-level energy use insights to personalize both digital and paper communications in support of home electrification goals.
At a foundational level, personalized outreach could include sending individualized monthly energy reports that show customers how their actual energy usage – itemized by time and appliance – is impacting their monthly utility bill.
Customer-facing web portals can also be enhanced to deliver insights about how a customer’s heat systems are impacting their energy costs and provide dynamic appliance-based similar home comparisons or calculate potential savings (CO2 and/or $) they could garner by switching to a more efficient appliance. When taking over incumbent programs, our utility partners often share that they have previously received customer complaints regarding inaccurate comparisons (different size homes, homes with different appliances, etc). Bidgely reverses this trend using a dynamic machine-learning based approach which results in comparing customers to more appropriate similar homes. Bidgely SHC algorithms dynamically recluster homes as heating fuel type or appliances change. This approach makes it possible for energy providers to realize simultaneous gains in CSAT and energy savings.
Energy providers can then layer on one-to-one outbound education and marketing outreach that highlights the most individually relevant benefits of heat pumps. Promotions can also include customized purchasing recommendations, such as those for rebates and installation professionals. And for inbound channels, Bidgely’s electrification insights turn call center agents into energy advisors who are better equipped to provide relevant information and recommendations when customers reach out with concerns about energy costs or questions about promotions they may have received.
The accuracy of our data science ensures that the insights and messaging customers receive are correct. Demonstrating a clear, measurable, short-term and easily attainable personalized appliance ROI is one of the most effective ways to accelerate beneficial electrification.
Presenting the right information to the right people is only half the equation. The other half is motivating them to take action, which happens by connecting with the needs and goals that matter to them the most.
Making the decision to electrify appliances is driven by a variety of personal factors, including economic and environmental motivations, and more. Energy providers can more successfully capture customers’ attention and prompt action using personalized marketing that aligns with each customer’s needs, unique circumstances, and values.
Customers who are motivated by sustainability, energy efficiency, savings, and comfort should be messaged differently. An environmentally conscious customer will find carbon reduction impacts most compelling, while a savings-minded customer is likely to respond best to messages about incentives available to mitigate the costs associated with converting. Further, customers who have already embraced solar and/or electric vehicles are likely to be interested in how switching to a heat pump will further beneficially electrify their life.
Bidgely’s analytics reveal customer lifestyle and value insights that provide utilities with the essential context to authentically connect with their customers with messages that resonate.
It’s important to note that utilities can also leverage customer energy use insights to drive other beneficial electrification measures that align with their own needs and goals, whether those are driven internally, or externally through variables like new regulatory requirements, investor relations or public expectations.
Data science empowers utilities to Personalize, Align and Motivate, thereby achieving home and building beneficial electrification goals on or ahead of schedule. Learn more about how AI can amplify your home and building electrification initiatives with our Heat Pump Adoption and Beneficial Electrification Playbook.
In India, some DISCOMs report losing as much as 25% of their total revenue to theft. In the United States, the US Energy Information Administration (EIA) estimates that electricity transmission and distribution losses were about 5% of the electricity transmitted and distributed in the United States between 2016 and 2020. In Jamaica, electricity theft is estimated to cost the Jamaica Public Service Company $200 million – 80 percent more than a decade ago.
Matt Copeland, head of policy and public affairs for National Energy Action in England, said in August 2022 that the problem is getting much worse. “The staggering increases in the cost of energy is leading to desperation. Some households… are resorting to electricity theft, which is illegal as well as dangerous.”
Beyond the substantial economic impact, theft impacts grid reliability by manipulating local area supply, which can lead to transformer overloading, blackouts, damage to utility assets, poor customer experience and safety vulnerabilities.
On every continent, theft is a significant challenge, with rising energy costs and new energy-intensive practices like bitcoin mining causing it to escalate even further.
With an expected 1.3 billion smart meters to be installed globally by 2025, energy providers now have the opportunity to leverage the trillions of granular AMI data points those meters produce to accelerate transportation electrification, improve customer engagement, advance decarbonization and enable bottom-up grid planning for greater resiliency and reliability. But for many, one of the most exciting applications of AMI data is revenue protection. In fact, combating non-technical losses is one of the primary drivers behind the massive 250M smart meter roll out now underway in India.
Historically, apart from basic meter hardware tamper alerts, broad-scale theft detection has been a top-down approach implemented at the grid level. This high-level view has been limited to assessing groups of homes associated with a single substation — for example, the 100 homes on a particular feeder/ substation — and flagging that group for review. The resulting investigation process often takes days or weeks to resolve in the field. Beyond that, there has been no consistently accurate means to pinpoint exactly which of those 100 customers are bad actors.
Now, however, Artificial Intelligence (AI) makes it possible to process data in hours, not days, and, like a detective, provide a clear, data-driven picture of exactly what is happening behind each meter. For the first time, energy providers are empowered with accurate and precise information about each meter upon which they can act to deter theft.
Bidgely’s AI-based analytics — backed by 17 energy-specific data science patents — center on disaggregating consumption data. This data science makes it possible for utilities to conduct theft analysis home-by-home, at the appliance level, rather than only at the transformer level.
By analyzing historical AMI data and correlating it with external factors like weather, AI-derived occupancy, and appliance/lifestyle profiling, Bidgely’s Energy Theft Detection Solution makes it possible to identify consumption-related anomalies that signal theft has or is occurring. Anomalies in appliance-level consumption patterns accurately reveal theft via meter bypassing, meter tampering, tariff misuse and more.
Meter Tampering
In cases in which electricity thieves take steps to prevent energy consumption from being recorded, our AI algorithms leverage energy consumption patterns, technical parameters of phase currents, neutral currents, voltages, power factors and available smart meter events to identify where and how meter tampering is most likely to have taken place. Tampering patterns include notable drops in consumption with corresponding electric anomalies.
Direct Theft
When consumers bypass the meter using an illegal connection mechanism, consumption patterns might flag such anomalies as when there is a change in outside temperature but no corresponding change in electrical use — i.e. when a heat wave occurs but there is no increase in cooling-related electrical use. Our analysis also looks at characteristic energy use for all homes with similar sanctioned/connected loads to identify significant outlier homes.
Tariff Misuse
Because smart meter data analytics isolate appliance-level signatures, it is possible to accurately distinguish between residential appliance behavior and commercial appliance behavior and identify patterns indicative of commercial activity on a residential tariff.
Targeting Bad Actors
Beyond smart theft detection, Bidgely’s advanced data science categorizes theft incidents and bad actors as high, medium or low probability and estimates how much energy loss occurred during an anomaly period. Our analytics also reveal the duration of theft activity and how many times the behavior was repeated — information a theft inspector cannot determine in a visit to a tampering site. This loss analysis enables utilities to focus mitigation efforts on the most significant violators and prioritize high probability and high value cases to ensure the highest possible return on investment to ensure the greatest theft mitigation success and return on AMI investment.
Bidgely has been selected by REC Ltd., a CPSE under MoP, GoI to participate in Ministry of Power, GoI Technology Incubation Challenge Powerthon 2022, which was organized in collaboration with SINE incubation lab of IIT Bombay. Our category is AI/ML based data analytics of consumer power consumption/ behavior and detection of theft for energy in support of the National Revamped Distribution Sector Scheme (RDSS) to reduce losses of State DISCOMs in India to 12-15% by 2025.
Through this challenge, we have engaged in a theft detection effort with a state utility to further demonstrate how AI-enabled data analytics can detect and resolve India’s energy misuse issues. At the same time, we’re working with a large utility in central India — in collaboration with the World Bank — to further prove theft detection analytics use cases.
Household-level energy theft detection enabled by artificial intelligence is setting a new standard. As the leader in energy disaggregation, Bidgely’s Energy Theft Solution enables utilities to not only see and understand loss with precision and accuracy, but also evaluate its severity and impact on revenue. Our results promise to significantly benefit utility revenue, grid performance, customer experience and safety.
To learn more about how Bidgely can help you successfully combat theft, visit our Energy Theft Solution knowledge center for a full library of solution briefs, playbooks, videos and more.
DER deployments will accelerate as cost of ownership continues to fall and wholesale markets unlock additional benefits, whether utilities choose to take an active role or not. And, the longer utilities wait to engage DERs at an enterprise level, the more difficult and expensive it will be to coordinate and manage rapid DER expansion on their grids.
Bidgely offers utilities a roadmap to successfully navigate — and more importantly benefit from — the DER revolution.
Energy providers need to know where DERs are located on the grid, their size, and how they’re being operated — preferably on a real-time or near real-time basis. This is especially true for customer-sited DERs and behind-the-meter DERs, where utilities typically have little to no visibility.
Applying sophisticated algorithms to AMI data makes it possible to learn how customer net generation is moving onto and off of the grid, and understand the characteristics of their residual behind-the-meter demand across household appliances and mechanical systems. Bidgely’s patented disaggregation algorithms powered by our UtilityAI® platform deliver a high fidelity profile of every end-use present in a household or small business. Utilities can determine with certainty if and how a customer is using a PEV, a rooftop solar system with battery storage, a heat pump water heater, and more.
Utilities typically rely on grid monitoring and analysis tools that focus on the distribution system and go as deep as the low voltage lines. That approach only shows half the picture. Demand data disaggregation and analytics are critical additions to the tool kit for any utility that’s serious about planning, deploying, and operating a two-way communicating, decentralized grid. Just because the traditional distribution system analysis shows you CAN invest in a distributed resource at a certain location, doesn’t necessarily mean you SHOULD.
For example, a hosting capacity analysis may show that a feeder can handle an additional 15 MW of distributed capacity, so a third party solar developer signs up to build it. However, disaggregated load data may reveal that most of the non-shiftable load on the feeder doesn’t match well with the duck-curve load the solar resource will provide. Or it reveals that there is an additional 10 MW of behind-the-meter DERs on the feeder that could be utilized to boost the hosting capacity to 25 MW, allowing additional technology types and configurations for utility DERs deployments on the feeder. Or, maybe the disaggregated data reveals a consistent end-use demand on a different feeder that more closely matches the export load shape of the solar resource offering a more efficient and effective deployment location.
Wholesale markets have already started opening to DERs this summer, and third-party aggregators are already on the scene signing up utility customers into their own wholesale programs. There is an urgent need right now for utilities to get a handle on who is participating and how. It’s possible that a utility that fails to fully engage in this opportunity might one day buy day-ahead, or spot market wholesale power aggregated from its own customers.
That example may seem far fetched, afterall FERC Order 2222 requires extensive coordination between distribution utilities, RTOs/ISOs, aggregators, and state regulators in order to streamline the integration of DERs into wholesale markets. And utility scale DERs deployments will be captured through interconnection agreements, and monitored through the inverter technology required to connect them to the grid.
But what about all the customer-sited and behind-the-meter DERs? Bidgely’s near-real-time DER usage behavior, pattern and trend data provides utilities and regulators with an invaluable single source of truth that ensures all parties are on the same page.
As well, this new policy reality is creating a blurred line between wholesale and retail markets, and it’s essential to equitably allocate costs between retail customers, DER owners, and aggregators. Bidgely’s data science empowers utilities to create granular and nuanced rate structures to successfully balance cost recovery — maintaining a safe, reliable grid system, and appropriately compensating DER owners for their resources. Bidgely’s DER insights inform responsive and hyper-targeted tariff structures to avoid a mismatch between rates and cost to serve, mitigating concerns over cross-subsidization and ensuring equity goals can be achieved.
Analytics-driven demand response programs have the power to alleviate congestion on transformers and substations in a more agile and scalable way by shifting the highest propensity end-uses within a given load profile and location.
For example, legacy demand-side management approaches might offer all customers within a congested area an incentive to shift their EV load. But not every customer in that area will have an EV, and among those who do, it is likely that many are already charging off-peak (or at public stations/work). Bidgely’s AI-powered data analytics allow energy marketers to pinpoint which customers have an EV and when they are charging at home. With those insights, energy providers can focus demand response programs to compensate these customers in proportion to their load contribution to realize greater grid benefit.
Demand response programs must also evolve from focusing unilaterally on reducing load to a two-way provision of flexible load and supply. BIdgely’s more agile and hyper-personalized approach to customer engagement plays an essential role in empowering utilities to influence where, when, and how customers use energy.
Current “business as usual” approaches to grid modernization and beneficial electrification are likely to take 20-plus years to complete, in direct conflict with more aggressive decarbonization timelines.
To accelerate this progress, Bidgely is working with utilities worldwide to leverage their AMI data to develop bottom-up, baseline views of their grids and plot roadmaps for truly distributed, two-way communicable grids. With a data-enabled foundation, it is possible to scale DER investments very quickly and shrink modernization timelines to 10 years or less.
Energy customers are deploying DERs at an increasing pace, and rules like FERC 2222 and massive investment programs like the IRA and IIJA are unlocking new revenue streams, which will have many third parties vying to engage customers in aggregation and integration programs that promise to ramp DER adoption even faster.
That’s why immediately harnessing demand data to catalog and deploy the variety of energy services DERs provide, and scale and optimize DERs across the grid is an imperative.
For more DER program strategies, download the full Navigating the Distributed Energy Resources Revolution: A Playbook to Guide Grid Modernization and FERC 2222 Compliance. Behind-the-meter, demand-side data has the power to enhance outcomes and increase the value of distributed resources utility-wide, but the time to act is now.
