There is no standard playbook when it comes to implementing time-based rates. Time of use pricing structures vary by region. Demand charge approaches vary by market. Critical peak programs vary by grid constraints. Every utility is at the cutting edge of program design with different approaches to solve the same fundamental grid load problems, enrollment hurdles, and affordability concerns.
Without proven best practices to follow, utilities often default to straightforward analysis. They look at current usage patterns and project bills under the new rate structure, segmenting customers into those who will save money and those who will likely pay more. Unfortunately, enrollment-driven approaches cause revenue leakage—the easy Savers join the rate, but don’t deliver the load shift. Auto-enrollment gets the shift, but your customers have bill shock after bill shock as the months tick by, clogging your call centers with angry and confused rate payers.
A binary approach misses the shades of gray, which includes all those customers who would pay more today on a time-varying rate, but could save significantly with targeted and specific behavior changes. In the short term, it constrains enrollment. But more significantly, over the long term, it overlooks the real opportunity to identify potential savers, coach them effectively, and continuously improve rate performance based on customer engagement and behavior change.
Let’s take a closer look at that third group of potential savers that traditional analysis misses.
This group might include a customer who charges their EV when they get home at 6pm. Under your new TOU rate, that’s expensive. But if they delay charging until 11pm, they save substantially. It also includes the customer who runs their pool pump all afternoon during peak hours. Shift that to overnight and the rate becomes beneficial. And it includes the customer who could pre-cool their home before peak pricing kicks in, reducing HVAC load during expensive hours.
These potential savers don’t show up in standard analysis. Identifying them requires true disaggregation that reveals not only what appliances customers have, but also when they’re running them. Machine learning disaggregation identifies specific appliance usage down to the time of day. You can see the EV charging from 6 to 8 pm. You can see the pool pump running in the middle of the afternoon. You can see the HVAC ramping up during peak hours.
That visibility changes your segmentation completely. Instead of winners and losers, you have winners, potential savers who become winners with coaching, and a much smaller pool of customers who don’t benefit at all from time-based rates.
Rolling out time-based rates successfully requires more than accurate modeling. You need an integrated approach across the customer journey.
1. Segmentation and Targeting
Start with premise-level disaggregation to identify which customers would benefit immediately as well as those who would benefit after implementing behavior change.
For the group of potential savers, define the personalized coaching they need, based on their actual usage patterns. “Shift your EV charging to after 11pm and save $40 monthly.” “Run your pool pump overnight instead of afternoons and save $25 monthly.” Make the behavioral changes specific, actionable and tied to real savings based on various rate plans and the actual customer usage.
2. Rate Education and Enrollment
Customers need to understand how the rate will impact them personally. Generic rate education won’t inspire participation. You need to show each individual customer their projected bill under the new rate – both if they continue their current behaviors, and after making recommended changes.
Multi-channel outreach helps, including via email, web portal, mobile app and SMS. Different customers engage through different channels. Your education needs to meet them where they are, in their preferred language.
3. Managing Bill Shock
Even with good education, bill shock happens. A customer may forget to adjust their behavior or seasonal factors could change usage patterns. They need to understand what factors triggered their high bill.
High bill explanations integrated into your customer service workflow help. With access to behind-the-meter usage data, your CSRs can see exactly what behavioral, weather or other factors drove the increase and can present relevant options for the customer to avoid high bills going forward. The same capability enhances the effectiveness of chatbots, IVR systems, and self-service portals.
The key is to provide more than an explanation, and instead teach customers how they can course-correct before the next billing cycle.
4. Measurement and Verification
Implementing time-varying rates requires continuous measurement at a granular level to track what’s working, including enrollment rates by segment, which messaging drives adoption, what type of coaching most effectively changes behavior and how much additional volume hits your CSR team.
This ongoing data collection enables you to refine messaging, adjust coaching, improve the customer experience and boost program outcomes.
Remember the challenge we set out at the start. Every utility is experimenting with time-based rates, without a standard playbook to follow. That creates risk. You’re investing in rate design, customer communications, and system changes without knowing if your approach will deliver the load management outcomes you need.
At Bidgely, our goal is to help utilities mitigate that risk by leveraging best practices and harnessing the behind-the-meter data that is essential to more effective targeting, coaching and program measurement.
Bidgely works with utilities around the world implementing different time-based rate structures, and that experience informs our platform development. Our patented true disaggregation technology provides the premise-level insights that enable accurate segmentation. Our data-driven customer engagement tools let you test different enrollment and personalized coaching strategies. Appliance-level visibility and bill explanation capabilities help your CSRs manage the transition. Near-real-time measurement and verification shows you what’s actually working. Rather than stitching together point solutions from different vendors, we’re working with our customer partners to deploy comprehensive solutions that handle the full customer journey from rate design through ongoing optimization.
And equally important, the lessons we’ve learned around the world help utilities validate and improve their own approaches, providing that time-based rate implementation playbook that utilities really need.
That’s how you move from experimenting in isolation to building a validated approach that unlocks potential savers. And that’s how you make time-based rates work for both your utility and your customers.
At Bidgely, we believe that using the right data can fundamentally accelerate how utilities operate and serve their customers.
Based on that belief, we’ve spent the last 15 years developing a thriving product ecosystem and core intelligence data fabric: the portfolio of products built on UtilityAI™, powering the experiences that our 40+ global utility customers rely on every single day.
But the future we’re building goes far beyond what exists today, and the future we’re facing bears little resemblance to the past behind us. Our industry is facing critical and competing objectives to service ballooning demand within the constraints of capital deployment, affordability considerations, and a constantly evolving consumer and industrial landscape. Utilities are adopting ambitious missions on accelerated timelines, and the scale of those ambitions requires engaging customers as active grid participants to partner with utilities to own our future.
That’s the obsession that drives our roadmap: putting Utility AI to work in entirely new ways, making data not just accessible, not just understandable, but actionable. We empower every person in your organization and every customer you serve to make data-driven decisions.
Our product roadmap is built around four foundational areas, each designed to help utilities navigate the complex challenges ahead while seizing new opportunities for innovation and customer engagement.
The trade press and other industry commentators have written extensively about horizontal AI, including ChatGPT, Claude, Gemini, and enterprise tools from companies like Bidgely partners AWS Bedrock and Glean. These systems consolidate organizational data from multiple resources and silos and make it accessible across departments. They’re powerful, offering democratization and personalization, increased understanding, and dynamic experiences, but they’re built with universal use cases in mind, not grounded in the deep specialization that our industry’s challenges demand from us.
Vertical AI is different. It’s specialized: The microphone on a piece of manufacturing equipment that listens for subtle noise changes that signal degraded performance, or the incredible imaging technology detecting cancers earlier than ever before. Every industry has unique applications where AI makes sense only in that specific context.
For utilities, vertical AI means actually understanding what’s happening behind the meter and how that usage drives the generic load shapes your systems see today. This is not statistical modeling, but rather true machine learning that reveals appliance-level insights with timeband accuracy and extensive metadata. Vertical AI for utilities can detect HVAC issues, understand solar panel capacity, identify revenue losses from theft, and create incredibly targeted customer segmentation.
With the launch of UtilityAI Pro, we’re unleashing the power of vertical AI for our industry.
UtilityAI Pro is architected to run on any compute engine. Whether you use AWS, Snowflake, Databricks, Azure, Google Cloud Platform, or even modern on-premises infrastructure, this platform lives right where you need it, wherever your data actually is. The result is complete freedom to innovate.
Beyond machine learning, we’ve enhanced Bidgely’s solutions with generative AI to make data more understandable, personalized, and actionable. Our GenAI and agents are designed specifically for the questions, concerns, and data that turns insights into real action.
Customer Engagement
Our customer experience products are rich with data visualizations. But a beautiful chart won’t have the desired impact if customers don’t understand it. We’re using AI to highlight key insights and enable customers to ask follow-up questions. “Why did I use more cooling?” can lead to a conversation about specific usage patterns, making the data truly accessible and the behavior modification more sticky.
We’re also moving beyond static behavioral recommendations to fully dynamic coaching aware of weather, time-of-use rates, true disaggregation across two dozen appliance types, and social proof from similar home comparisons. Life is messy and infinitely varied, and utility-customer communication should be designed with that reality in mind.
Grid Operations
Bidgely is leveraging AI to build agents that monitor data continuously, essentially “tapping you on the shoulder” when trends change meaningfully. These agentic alerts monitor thresholds, trend violations, and anomalous behavior across all your data.
Utility teams no longer have to check dashboards daily. Instead dashboards alert those teams when something important happens.
In addition, we’re developing AI capable of strategic deep research that synthesizes custom insights about your grid based on your strategic objectives. In this way, we’re able to convert noisy utility-wide data into the most impactful, highest velocity insights every quarter.
I often say that our utility partners are running both a rat race and a marathon at the same time. Short-term, it’s critical to manage daily challenges like anomalous weather or simultaneous EV charging and transformer capacity. But long-term performance is just as important, guiding capital deployment decisions to operate not just next-year’s grid, but also the grid of the next-decade.
Our Distribution Grid Planning product puts actionable, powerful tools in the grid operator’s toolbox:
Imagine, for example, watching a map where green assets gradually turn yellow and then red as you move the clock forward ten years. You can drill into each one, understand the capacity deficit, and build targeted programs customized for specific areas and customer segments. This is the power of Bidgely’s AI-powered grid planning tools.
Earlier, I mentioned that customers must be active grid participants. Making that a reality requires personalized engagement at scale, understanding individual circumstances and delivering the right message at the right time. Our customer engagement roadmap focuses on two areas where this matters most: energy affordability and rate optimization.
Affordability Support
Energy affordability remains a critical concern for utilities, policymakers, and consumers alike. Rising utility bills, particularly among income-qualified households, have intensified calls for comprehensive solutions.
Bidgely offers a data-driven approach with solutions to help boost both enrollment and impact.
UtilityAI leverages both demographics and usage data to identify income-qualified customers with much higher confidence than zip code or even census block analysis alone. By layering in proprietary data science, Bidgely provides household-level predictions to feed better, more actionable leads for program implementers.
Before and after enrollment, UtilityAI makes engagement more effective through multi-channel communication, generative AI automatic translation into languages like Spanish and Vietnamese, and insights derived from income surveys. It’s possible to target communications across channels (Paper, Email, SMS/WhatsApp) addressing customer-specific concerns like energy burden, arrears control and more.
Time of Use Rates
Bidgely’s AI does more to help customers through the complicated transition to time-of-use rates.
We make it possible to provide personalized, time-aware, contextual coaching and explanations around high bills and TOU adoption, from promotion and enrollment through ongoing behavior change, including:
Vertical AI, generative AI and agentic AI grid planning and customer engagement advances are just the start of our roadmap. We’re also moving forward with real-time disaggregation with partners like Itron for AMI 2.0, improved Level 1 and Level 2 EV detection accuracy, enhanced theft and revenue protection, advanced HVAC fault detection and more.
For those who’ve been early adopters and beta customers, thank you. You’re helping shape products that work for your organization and your peers. You’re working directly with our product managers and engineers to drive the roadmap forward.
For those considering it: say yes when we reach out. Or, reach out to our team. It’s an incredible opportunity to influence the future of utility innovation.
The right data, properly understood and put to work, truly can unlock a transformational future. I can’t wait to see what we build together.
The most-forward thinking utilities are embracing the power of AI and big data to chart the most efficient and effective path to achieve future readiness.
Bidgely has been with Avista Utilities on its AI journey from the start. Providing electricity to nearly 403,000 customers and natural gas to ~369,000 customers across 30,000 square miles in eastern Washington, northern Idaho, and parts of southern and eastern Oregon, Avista is executing on multiple big data and AI use cases across functional areas, deploying them successfully, and scaling them across the organization for maximum grid benefit.
I had the opportunity to talk with Andrew Barrington, Products and Services Manager at Avista, to get his perspective on the utility’s journey so far, and how he sees big data continuing to transform their operations.
Andrew says Bidgely’s UtilityAI™ Analytics Workbench tool has acted as a “use case generator” for the utility. As departments share information and data-driven discoveries, it inspires new questions and prompts the rethinking of legacy problems with new approaches.
“It’s not just a single individual or a single department. It has to be cross-functional. It has to be a conversation amongst your utility,” he notes. “I’m proud to say that Avista has achieved that. We have a lot of awareness around big data. We have a functional group of about 15 users, and we meet with Bidgely every other week to come up with new use cases and talk through how we can take the data we have and elevate it further. We are able to continuously ask questions like, ‘Can we track what the potential growth is?’ or ‘Could we learn a little bit more about where this trend is going?”
In the past, Avista used in-house methods to create EV charging load profiles, which were informative but had limitations. These methods were restricted to known EV customers in utility programs, sometimes lacked accuracy, and demanded substantial engineering and analysis work.
I asked Andrew to share his view as to how the advanced features in BIdgely’s UtilityAI™ Analytics Workbench tool have enabled Avista to produce robust load profiles at scale, and gain deeper insights into customer charging habits, feasible load-shifting strategies, and smarter grid infrastructure planning for future EV demands.
“In 2021 we worked with Bidgely to analyze our AMI data to detect just over 600 accounts. But now we can see our growth over time to 2022 and 2023. In just three short years, we saw a growth of close to 1,300-1,500 vehicles come into our service territory. That’s fantastic insight for us to understand our EV growth. And what’s really interesting is each year EV time-of-use shift has shifted. The time of use when we started is much different than where we’re at today.”
Beyond total EV load, Avista is able to identify feeders and transformers that could come under strain due to EV proliferation.
“We have been able to leverage our meter data a step further and pinpoint our EV driving customers to specific substations, and start to determine the related strain at the meter and substation level, and even more granularly to the feeder level. In fact, we are working on going even further and getting to the transformer level for all of our detection and for our grid team.”
As part of that analysis, Andrew says Avista is better able to anticipate how EV charging is growing regionally — community-by-community.
“Our Spokane, Washington, AMI territory is very different when you look at communities in the north, south, east or west. We looked at a feeder in our South Hill region—which is typically higher income—and we saw that the EV adoption rate was five-to-one compared to communities in north Spokane. Now with that insight, we’re able to get to the feeder and substation level and identify varied growth models across our entire service territory. So, rather than having to enhance our grid across the board, instead of ripping the Band-Aid off and having to do
everything all at once, we can strategically start moving and enhancing our grid resiliency in alignment with where the growth is actually happening now and most likely to happen next.”
Active load management is another of Avista’s critical initiatives. Andrew says he appreciates that Bidgely’s Analytics Workbench allows the team to identify which customers are charging their vehicles during peak and off-peak hours.
“We can see that out of 2,300-ish EV owners, more than 2,000 are charging on-peak right now. We don’t have time-of-use rates yet, but we will in the future. So it’s very interesting to see the opportunity that we have to shift some of that load.”
Beyond grid planning, Andrew says the utility’s EV intelligence has also revolutionized its program marketing efforts.
“We had a program in 2023 through which we were paying a significant portion of the cost to enable customers to install an Avista level-two charger within their home. It was a strategic investment designed to enable us to learn more about charging behaviors and have the potential to control usage. But we only had 700 participants in that program, and we knew we had more than 700 EV customers within our service territory. We tried customer outreach, we tried doing surveys. We didn’t feel that the motor vehicle Department of Licensing was a path we wanted to go down. So we turned to Analytics Workbench, which was able to identify 1600 additional EV customers who were charging on our system. We could then target marketing campaigns to those customers to share awareness about the EV tools and resources that we were offering. Within hours, we provided corporate communications with a hyper-targeted list of account numbers, addresses, household energy usage, and their trending behavior.”
Managing HVAC load is another program area that has benefited from behind-the-meter intelligence.
“One of our first Analytics Workbench use cases was to identify which customers within our service territory were high or low efficiency in terms of HVAC. We found some of our customers were using 12,000 kWhs a year in heating, compared with customers who had very high efficiency appliances who were only using around 4,000 kWhs per year. That’s incredible. That empowered us to direct market to those customers with low-efficiency appliances. We could identify which feeder and what substation they were on, and then just fully replace their appliance. I’m able to knock on the door and give them information they need.”
Two use cases that Andrew was particularly excited to discuss are tied to the utility’s equity efforts.
When the USDA announced new grants to help utilities lower energy costs for rural communities, Avista initially didn’t think they had any communities that would qualify.
“When the federal grant came out to support customers within your service territory that spend more than 275 percent of the national average on energy with a grant, at first we didn’t think we’d have anyone who would be eligible because our rates are too low. But we ran the analysis with Analytics Workbench and within 10 minutes were able to identify 90 customers within one zip code that were qualified. So now we’ve got grant writers working on submitting for these grants. Without this tool, we would have discounted it. We would have said no to the opportunity.”
“We also have a Clean Energy Transformation Act (CETA) in Washington State that is focused on our low-income, vulnerable customers. We gave Bidgely a list of 150,000 accounts that are within that named community and were able to identify customers within the top 30 percent of heating usage or cooling loads. Now, we can go in and completely replace those appliances for those customers.”
We hold bi-weekly working sessions with the Avista team to brainstorm new use cases and explore how to capture more value from behind-the-meter data to address their most pressing challenges. Andrew says those conversations, together with the discussions they now regularly have internally, are key to Avista’s continuing innovation.
“A big piece of our success is the cross functional collaboration this data allows. In my role, I am heavily involved with our energy efficiency, account and regional business managers, so I get looped into a lot of different conversations where my colleagues are saying, ‘It’d be great if we could do this.’ I get to raise my hand and say, ‘Hey, I think we can.’ It’s critical to make meter data available as a use case generator across the organization. You can’t just silo it within a single department, you have to share that awareness to achieve the maximum benefit.”
To hear more from Andrew Barrington about the Avista AMI data-driven approach, watch our on-demand webinar Seize the Data: Optimizing Grid and Customer Programs in 2024. To explore how to leverage AI and big data to improve future-readiness at your utility, connect with our team at [email protected].
Recently I had the chance to speak with Patty Torrez, Manager of Digital Customer Experience, and Denine Rothman, Time-of-Day Program Manager, from Public Service Company of New Mexico (PNM) about how they are navigating the complex transition from traditional monthly billing to dynamic pricing structures and grid modernization.
They told me that PNM’s journey toward grid modernization began well before their plan received final approval from the New Mexico Public Regulation Commission in fall 2023.
“We are in a monthly meter data environment currently, and so we do not have any AMI meters deployed,” Torrez explained. “Our grid mod plan was approved last fall by the New Mexico Public Regulation Commission, but the time-of-day pilot was actually conceived about 18 months before that.”
What struck me about PNM’s approach was their foresight in pursuing parallel tracks. While working through the lengthy regulatory approval process for comprehensive grid modernization, they simultaneously developed their Time-of-Day pilot program through a separate rate case.
“The TOD pilot was actually proposed as part of a rate case, and was approved separately from our grid mod plan. The folks on our pricing team started thinking about it, probably about the same time that we actually submitted our grid mod plan to the PRC,” Torrez added, highlighting how this strategic separation allowed PNM to proactively begin customer education and behavioral change initiatives well before full smart meter deployment.
“We implemented our Time-of-Day program at the beginning of January of 2024,” said Rothman. “Currently, we have 1200 customers who have opted into the program.”
The pilot features an innovative bill guarantee mechanism, which Rothman said has been central to the program’s success.
“If a customer spends more on the TOD rate versus what they would spend on the residential rate they were on before the pilot, PNM will credit them the difference after 12 consecutive months of participation,” she explained.
This approach effectively removes customer risk while allowing PNM to gather valuable data on usage patterns and behavioral change across diverse customer segments.
Contrary to the assumption that smart meters automatically drive customer savings, Rothman emphasized that technology alone is not the answer. Customer education has been a critically important part of their solution.
“When we first kicked off this program, a lot of people were excited to get the cellular meter for free and thought it would do some sort of magic,” Rothman shared. “But the magic is not in the meter. It’s in the education and the behavioral shifts that you do that deliver the results.”
She said it’s this philosophy that has shaped PNM’s approach to customer engagement, focusing on customer enablement rather than just technology deployment. Participating customers receive Bidgely insights that itemize their energy usage by appliance, by day and by hour. They also receive budget alerts.
“We want to be able to empower and educate our customers. We want to equip them with the tools and resources to save money on peak hours,” she said.
“The interval usage data and this TOD pilot are really giving our customers a lot more insight into how they use their energy and the control that they have to make changes and to start using their energy differently so they can see those savings,” added Torrez.
PNM had an inclusive opt-in strategy for the pilot, which has attracted unexpected participants. For example, 98 solar customers were able to enroll, despite uncertainty about potential savings for this segment. They’ve also focused on education and awareness within low- and fixed-income segments. By including all customer types, PNM has been able to gather more comprehensive data for future rate design.
When it comes to impact, PNM has seen impressive pilot results across customer segments, with remarkable retention and savings.
“With our commercial customers, we have a 0% cancellation rate. And year to date, our commercial customer participants have saved over a million dollars just by being on the rate,” Rothman reported.
But perhaps even more compelling was a story she shared about a residential customer who achieved extraordinary savings through family-wide behavioral change.
“I just talked to a gentleman who actually saved over $1,000 in a year by engaging his whole household in the effort,” she said. “It was a family effort race. Once we educated him about when the off-peak and on-peak hours were, he got his kids on board,” Denine explained. “They were just very conscious as far as the time that they were using their bigger appliances, and really just conscious about their energy usage, as far as using appliances, turning off lights, unplugging their devices… those types of things.”
Torrez and Rothman emphasized how the pilot is serving as a crucial stepping stone, reinforcing that customer experience has to be the guiding principle. The lessons learned from the pilot are directly informing their approach as AMI deployment will kick off in phases next year.
“We can’t control rates, but we can control the experience our customers have and the tools and the resources that we provide them to improve that experience,” Rothman said.
This focus extends to proactive customer support, she added.
“We’ve had a lot of success stories as far as people calling and wondering why their bills are high. And before we had appliance-level analytics, we didn’t have the insights to definitively explain the cause – to be able to say, ‘it looks like you have an electric vehicle.’”
Torrez agreed that the benefits of the pilot have accrued to customer support, not only when it comes to customer satisfaction, but also by improving operational efficiency.
“The proactive communication that our TOD pilot customers receive in their monthly summary emails drives down calls to our contact center, and decreases the length of those calls,” she said.
PNM’s pilot approach reveals a number of helpful lessons for utilities in similar circumstances.
The Time-of-Day pilot demonstrates that with proper planning, customer education, and risk mitigation, utilities can successfully introduce dynamic pricing while maintaining – and even improving – customer satisfaction. The transition to more sophisticated rate structures doesn’t have to be disruptive. It can be empowering for customers and beneficial for the grid when executed with care and strategic vision.
As PNM prepares for full AMI deployment and permanent TOU rate structures, their pilot continues to provide valuable insights that will benefit not just their customers, but the broader utility industry.
To hear more from Denine and Patty, watch the full-length webinar on demand.
Technology leaders across the utility industry recognize the limitations that data silos and disparate information create within the organization. With this in mind, many utilities have invested heavily in next-generation cloud data platforms, like Snowflake, to centralize AMI, CIS, GIS, and other data streams to establish a consistent data foundation across operational areas. When paired with the power of generative AI, these investments can accelerate cross-functional collaboration and improve efficiencies.
These foundational data investments are essential steps forward. However, unlocking their full potential requires deep domain expertise and specialized customer intelligence that enables business units to deliver personalized customer experiences, ensure affordability, and power a reliable grid.
That’s why we’re excited to announce that Bidgely is a launch partner for Snowflake Intelligence. This partnership will connect Bidgely’s UtilityAI Pro models and MCP servers to Snowflake Intelligence, enabling utility-specific use cases and AI agent development while running securely in the utility’s own cloud environment.
UtilityAI Pro™ packages Bidgely’s patented machine learning models – backed by billions of data points and over a decade of data science research and development – and securely deploys them directly in the utility’s cloud environment, such as Snowflake. Without requiring any external data transfer, utilities gain access to a foundational layer that enables utility-specific AI agents, integrations with existing utility tools, and development of custom applications.
Bidgely UtilityAI Pro™
Snowflake Intelligence enables conversational AI with structured and unstructured data sources from across utility and third-party data sources. With Snowflake Intelligence, utilities can interact with data in natural language and intuitively build data agents, while leveraging any leading gen AI model.
Combining governed data in Snowflake with utility-specific intelligence from Bidgely’s UtilityAI Pro™ enables AI agents to answer questions specific to the utility industry, such as:
In the example below, a DSM Program Manager agent, designed with the specific goal of designing cost-effective programs, helps a utility team member identify the highest value customers to target for enrollment in a new program.
DSM Program Manager Agent in Snowflake Intelligence
These agents allow utility teams to accelerate project development, unlock operational efficiencies, and focus on more strategic avenues for serving their customers
Find out how to unlock ROI, reliability, and resilience across your utility. Contact the Bidgely team for an overview and demo of the solution.
Extreme volatility in the energy wholesale price is keeping European energy retail executives worried!
The scale of the challenge is unmistakable. According to the International Energy Agency (IEA), energy markets in Europe have had a volatile start to 2025, with prices surging to their highest level in two years.
Wild price swings create chaos for energy retailers, and when wholesale costs spike unexpectedly, suppliers face difficult choices about how to respond: When wholesale prices spike unexpectedly, suppliers must decide whether to absorb the costs or pass increases through to customers. Both options are problematic. Absorbing costs hurts the business, whilst price increases drive customer churn.
The challenge is compounded by customer expectations. Most energy customers have little tolerance for sudden bill increases, putting retailers in a difficult position when wholesale costs surge.
However, forward-thinking energy retailers are leveraging a powerful tool available today to provide a hedge against wholesale chaos: customer-side demand flexibility.
The solution to the market volatility challenge lies in transforming customers from passive energy consumers into active flexibility partners.
According to Eurelectric, consumption flexibility can be defined as “temporarily increasing or reducing electricity consumption based on grid conditions and market signals.” This flexibility requires more than smart thermostats and off-peak electric vehicle charging, though both play an important role. True demand flexibility requires building systematic capabilities to holistically influence customer consumption patterns in response to wholesale market conditions.
When wholesale prices rise, retailers with effective demand flexibility programmes can reduce their customers’ aggregate consumption, lowering their exposure to high-cost energy purchases. When wholesale prices drop, they can encourage increased consumption to take advantage of low-cost periods.
Retailers with effective TOU tariffs, managed charging or other demand flexibility programmes are able to reduce their customers’ aggregate consumption when dealing with predictable variations, such as peak vs. off-peak periods, during which price changes are expected.
Further, in the case of unpredictable volatility, in which wholesale prices suddenly spike or plummet due to unforeseen events, responsive demand flexibility becomes even more critical.
The goal in both scenarios is to create a natural hedge against volatility whilst providing value to customers through reduced bills.
Successfully implementing demand flexibility requires moving beyond ad hoc appeals for conservation during crisis periods. Instead, retailers need systematic capabilities to influence customer behaviour both proactively and responsively.
Proactive Demand Management
Time-of-Use tariffs represent the foundation of proactive demand flexibility. However, effective Time-of-Use programmes require sophisticated customer engagement to help consumers make energy choices that maximise savings and load shift.
Electric vehicle managed charging programmes offer another powerful tool for proactive demand management.
Responsive Demand Flexibility
Responsive demand flexibility involves real-time adjustments to consumption based on immediate market conditions. When wholesale prices spike unexpectedly, retailers with responsive capabilities can quickly reduce customer demand through targeted communications, smart device controls or demand response incentives.
Achieving responsive demand flexibility requires sophisticated communication capabilities and customer segmentation by appliance ownership, contribution to peak load, time-of-day usage patterns, and historic responsiveness. Different customers respond to different types of appeals, such as cost savings messages, environmental benefits, and convenience features. Effective responsive demand management delivers the right message with the right incentive to the right customer at the right time.
Demand flexibility programmes succeed or fail based on customer participation, which is directly proportional to customers’ level of engagement with their retailer. Getting customers to actively manage their energy use requires reimagining customer experience around 4 core principles:
1) Hyper-Personalised, Appliance-Specific Engagement
Generic energy-saving messages don’t drive any meaningful engagement nor any behaviour change. Customers need accurate, relevant insights delivered through their preferred channels. If energy use and cost insights aren’t trustworthy or personally relevant, customers won’t engage, and their relationship with the retailer will remain transactional.
This means moving beyond mass communications to personalised engagement based on individual energy use habits, appliance ownership and demonstrated preferences. For example, a customer with supplemental electric heating needs different messages than one with a heat pump, and a household with solar panels will respond to different incentives than one without. AI-powered behind-the-meter data analysis can identify which customers have the greatest flexibility potential based on their unique energy profiles.
Effective demand flexibility programmes connect behavioural calls-to-action to optimal timing. For example, if a customer is trending toward a high bill, it’s an ideal moment to encourage conservation and promote Time-of-Use tariffs or managed charging programmes.
This requires sophisticated analytics to identify the right moments for engagement. Successful programmes don’t wait for monthly bills to arrive. They provide real-time insights and recommendations that help customers make better decisions in the moment.
3) Trust Through Transparency
Customers participate in demand flexibility programmes when they trust their energy supplier and understand the benefits. This requires transparent communication about how programmes work, what customers can expect to save and how their participation contributes to overall system reliability.
Building this trust takes time and a proven track record for delivering on promises. Retailers who over-promise savings or fail to deliver on programme commitments quickly lose customer confidence, engagement and participation.
4) Continuous Improvement
Real-time analytics allow retailers to continuously measure and optimise demand flexibility performance among the customer target group. Understanding which messages drive behaviour change, which customers participate most actively and which programmes deliver the greatest wholesale cost savings enables continuous improvement and expansion.
Energy retailers who build systematic demand flexibility capabilities will enjoy significant advantages in the midst of volatile wholesale markets. Lower exposure to price spikes translates directly to improved financial performance. Plus, stronger customer relationships built through valuable energy management services reduce churn.
Perhaps most importantly, demand flexibility capabilities create differentiation that extends beyond price competition. Customers who actively participate in their supplier’s flexibility programmes develop sticky relationships that are difficult for competitors to replicate.
Wholesale market volatility isn’t going away. Climate change, geopolitical tensions and the energy transition ensure that European energy markets will remain challenging for the foreseeable future. Rather than remaining vulnerable to these market forces, energy retailers can build capabilities that transform this volatility into competitive advantage.
The suppliers who will thrive in volatile markets are those who stop viewing customers as passive consumers and start empowering them as active flexibility partners. Customers continue to demonstrate their appetite and appreciation for energy management tools. Empowering them with the insights to take action strengthens customer relationships and reduces wholesale exposure through sophisticated demand flexibility programmes.
In fact, it’s possible to turn volatility into an asset with Bidgely:
To learn more about how Bidgely turns market volatility into opportunity and see how AI-powered demand flexibility works in practice, explore Bidgely’s personalised customer engagement and demand flexibility solutions in our demo portal or contact us for a chat.
The European energy retail market is trapped in a vicious cycle. Customers switch providers to gain the smallest cost savings, suppliers slash margins to compete, and genuine differentiation becomes nearly impossible. This commoditisation crisis threatens to trap the entire sector in an unsustainable race to the bottom.
But recent customer research suggests there’s a better path — one that isn’t driven by price alone.
The numbers tell a stark story. In 2024, Eurelectric and Accenture surveyed more than 2,000 energy consumers and 60+ suppliers across 12 European countries. Nearly one in three consumers reported having switched energy suppliers in the past two years — primarily to save money. This represents an eight percentage point increase since 2021, and the trend continues to accelerate.
Looking ahead, one in four consumers plan to switch providers within the next year to save on their energy bills. For energy retailers, this creates a punishing economic reality where customer acquisition costs continue rising whilst customer lifetime value diminishes.
Here’s what’s encouraging: the same Eurelectric study reveals that consumers also increasingly appreciate more than low-cost energy. Nearly half of surveyed consumers are also aware of their energy supplier’s offerings to help manage energy bills, and 28% report feeling more positively in control of their energy usage, thanks in large part to tips and advice from their suppliers.
This data points to a fundamental shift in consumer expectations. Today’s energy customers don’t just want to pay less; they want to understand their consumption, make smarter decisions, and gain genuine control over their energy costs.
The solution? Deliver service that transforms you from a commodity energy provider into a valuable, trusted energy advisor in the eyes of your customers.
Successfully making this transition requires moving far beyond generic energy-saving tips that are often off the mark. True differentiation comes from providing insights and recommendations based on each customer’s specific appliance ownership, consumption patterns and unique household energy profile.
This level of personalisation addresses what Eurelectric identifies as a key market challenge: “Smart energy technologies offer strong potential to help consumers manage costs, yet adoption is limited by lack of interest, awareness, and perceived relevance.”
The key word here is “relevance.” Customers need advice that speaks directly to their unique circumstances.
Two AI capabilities are revolutionising how energy suppliers can deliver personalised advisory services:
Meter Data Disaggregation
AI-powered true disaggregation of smart meter data is able to identify the energy consumption signatures of individual appliances within a given household. This provides consumers and customer service agents with unprecedented visibility into behind-the-meter energy use, across every hour of the day. Instead of receiving a total monthly bill, customers are able to see exactly which appliances and energy habits are primarily responsible for their costs.
Generative AI Interfaces
Natural language AI makes these complex insights accessible to everyday consumers. A customer can simply ask, “Why did my cooling costs increase compared to last year?” and receive a personalised explanation along with specific recommendations for reducing future costs. This conversational approach to energy data democratises access to sophisticated insights whilst reducing the cost-to-serve associated with contacting the call center for the same insights.
When energy suppliers provide genuine value through personalised insights and advice, it improves customer retention. Customers who rely on their supplier’s energy management tools and advice are less likely to switch to obtain marginal savings. The relationship becomes stickier when it is based upon a value proposition of ongoing service and support, not just commodity provision, breaking the cycle of price-only-based decision making.
Energy suppliers who embrace the hyper-personalised, energy advisor model now will enjoy significant competitive advantages: lower customer acquisition costs, reduced churn, sustainable margins, and stronger customer relationships.
The European energy retail sector stands at a crossroads. Suppliers can continue down the path of commoditisation and margin erosion, or they can seize the opportunity to redefine their role in customers’ lives. The choice, and the competitive advantage, belongs to those who act first.
To see what personalised AI-powered customer engagement looks like in action, check out our demo portal and read our case study about how Electric Ireland has maximised the value of data to deliver a superior energy customer experience.
Small-scale energy generation and storage distributed energy resources such as solar PV, wind turbines, batteries and EVs (batteries on wheels) have little grid impact individually. But with scale their grid impact surges.
Millions of consumers have already adopted solar, batteries and/or electric vehicles. More importantly, thanks to cost reductions, product improvements and a desire for greater resiliency, the number of DERs is expected to reach tens of millions by the end of the decade with ~1 TWh of capacity to generate, consume and store energy.
This capacity surge offers tremendous potential benefits. DERs can stabilize the grid through virtual power plants and help utilities generate more revenue through electrification than they spend on energy generation and distribution.
However, rapid DER growth also presents serious challenges. With so many DERs operating independently, demand-supply imbalances at the local level will become a reality playing havoc on the grid and with electricity rates.
The solution lies in AI-powered grid planning and management, which makes it possible to harness DERs’ positive potential while mitigating risks across all categories.
The primary EV challenge utilities face is peak demand contribution. When drivers return home from work and plug in their 8-12 kilowatt chargers, the demand on the grid multiplies exponentially.
Compounding this challenge, EV adoption doesn’t occur uniformly across territories. Instead, it happens in pockets with dramatic variation from zip code to zip code, even block to block. This uneven pattern creates congestion hotspots at the distribution level, overloading some transformers and substations while leaving others underutilized.
Smart Targeting Strategies
Traditional non-targeted managed charging programs suffer from low enrollment because of their invasive nature. Moreover, customers who enroll may not be the biggest contributors to grid stress, leading to low return on investment.
The answer lies in precision targeting. A blend of behavioral and direct managed charging initiatives that target peak users in areas where the grid operates near maximum capacity engages the broadest range of EV drivers while dramatically amplifying infrastructure benefits.
AI-driven behind-the-meter EV intelligence enables utilities to identify owners most likely to alleviate load at congested grid assets. This intelligence helps utilities target incentives effectively and engage drivers in the full spectrum of programs—behavioral managed charging, EV time-of-use rates, and direct managed charging.
By building trust and improving customer relationships, utilities increase the likelihood that drivers will grant some control over their charging activities. More effective customer-program alignment can reduce the cost per kilowatt and nearly double EV hosting capacity on the distribution grid.
With solar systems, utilities must flatten the “duck curve” — the phenomenon where solar produces peak energy midday when demand is lower, then stops producing during evening peak demand after sunset.
Flattening the curve means tapping into the energy stored in these distributed resources. This begins with identifying residential customers with rooftop solar, including array size and daily and seasonal generation patterns. Advanced disaggregation technology can detect how household net generation flows into the grid.
Storage as the Solution
As with EV programs, targeted participation from solar customers based on geography and generation capacity is essential. Pairing solar with batteries that store daytime energy provides utilities with access to distributed, reliable power during peak demand.
Utilities need energy storage assets where the grid faces the greatest strain and requires load shaping or resiliency support. Current storage deployment patterns may not align with grid demand, making data-driven program targeting crucial.
Behind-the-meter disaggregation helps utilities incentivize customer adoption in areas where residential storage provides maximum benefit while gaining control of these assets. Virtual power plant programs demonstrate the effectiveness of combining customer-supplied batteries with dynamic incentives and device control mechanisms.
Accelerating DER adoption appears inevitable. In some regions, solar and EV adoption already exceeds 30 percent. Without proper management, DERs risk creating significant costs for customers and energy providers alike. With effective management, they promise improved grid stability, equity, and utility revenue without increasing overall costs.
Success across all DER types requires six critical best practices:
Through advanced disaggregation technology, utilities can understand energy demand and supply at the household level, enabling purpose-built strategies for each DER type.
Learn more about Bidgely’s AI-powered solutions to help you manage DERs and see our DER Grid Planning solution in action in our demo portal.
In today’s rapidly evolving energy landscape, utilities face unprecedented challenges with increasing DERs, electrification and changing customer behaviors. In response, forward-thinking energy providers are increasingly turning to Non-Wires Alternatives (NWAs) as compelling solutions that go beyond traditional infrastructure investments.
“Historically, we have looked closely at how our traditional wires solutions have helped our customers to maintain reliability,” explains Dhaval Patel, Senior Network Manager at Hydro One. “But because of the changing nature of the electrification load and customer behavior, we have started looking for non-wires solution alternatives, and data is a big help in identifying the best use cases for NWAs.”
This shift in approach isn’t just about managing current challenges — it’s about positioning utilities for future success. Over the next decade, utilities that effectively leverage NWAs and data-driven capital investment strategies will enjoy significant advantages: stronger ROI, reduced operational costs and more resilient business structures.
1. Enhanced Forecasting Capabilities
The foundation of effective grid management starts with accurate forecasting. By developing a behind-the-meter understanding of appliance ownership and usage patterns, utilities can create comprehensive territory-wide load profiles with unprecedented detail.
By combining these detailed load profiles with broader housing and appliance ownership trends, utilities are able to forecast peak load 5-15 years into the future with reliable accuracy. This granular approach also facilitates scenario analysis for various electrification pathways — revealing exactly how and when heat pumps, EVs and other DERs will impact the grid.
Patel confirms that Hydro One is pursuing just such a strategy: “We have been collecting a lot more customer data,” Patel explains. “However, primary monitoring and data collection points were at the feeder or station levels, but we never looked behind the meters. Now, by analyzing behind-the-meter customer behaviors, we try to sense how many electrical vehicles customers are adopting.”
2. Targeted Non-Wires Alternatives Programs
With AI-powered analytics, utilities can examine load curves across their grid assets to identify maximum, minimum, and average demand patterns. This precision reveals exactly which substations are approaching capacity constraints and which specific appliances are driving peak demand.
Armed with this intelligence, utilities can design highly effective NWA programs that:
“Now we are looking more at customer behaviors and customer engagement,” Patel notes. “Looking at the behind-the-meter behaviors helps us understand how it integrates into the load profile, how it impacts our stations, and how we can better manage our stations and load profiles at a micro level.”
Consider the practical application: By analyzing household consumption patterns, utilities can identify customers who charge electric vehicles or run pool pumps during peak hours and target them specifically for load shifting programs — creating immediate benefits for both the customer and the grid.
3. Optimized Infrastructure Investment
While NWAs offer powerful alternatives to traditional infrastructure, strategic grid investments remain essential. The challenge is knowing exactly where and when to deploy capital for maximum impact.
“We are trying to understand the heating electrification impact on our grid as heat pumps are becoming really popular in Ontario. This data helps us to accurately forecast load, which helps identify the future grid investments we’ll need to make and the constrained areas that we need to alleviate.”
Using a comprehensive data approach, utilities are able to:
The benefits extend beyond traditional grid assets. With insights into current and future EV adoption patterns, utilities can strategically place public charging infrastructure in locations that will deliver the greatest value.
The energy landscape is transforming rapidly, driven by electrification, customer behavior shifts, and climate imperatives. Utilities that embrace data-driven approaches to grid management will be best positioned to navigate these changes successfully.
By developing comprehensive visibility into both grid-level and behind-the-meter dynamics, energy providers can balance reliability with cost-effectiveness through a strategic mix of NWAs and targeted infrastructure investments.
As Patel emphasizes, “Data serves as a basic foundation.” That foundation enables utilities to build a more resilient, flexible, and customer-centered grid that will meet the challenges of today while preparing for the demands of tomorrow.
The future of energy is already here—and it’s being shaped by those who understand that in an increasingly complex grid environment, data-driven decision making is the key to success.
For more information about Bidgely’s DER grid planning capabilities, download this guide to Bottom-Up Grid Planning Through Behind-the-Meter DER Intelligence.
As electric vehicles (EVs) reach mainstream customers, utilities are facing challenges in serving demand growth from EV charging, particularly on the distribution level. According to recent projections, EV adoption is expected to grow from 4.8 million EVs on U.S. roads today to 78.5 million by 2035 — representing more than 26% of all cars and light trucks. While recent market shifts due to tariffs and potential changes to EV tax credits may affect this projection, utilities are already experiencing substantial impacts directly attributed to EVs, which means that identifying and understanding EV-driving customers has never been more important for utilities to mitigate grid constraints.
In fact, unmanaged EV load has the potential to require billions* of dollars in secondary transformer and service upgrades, all while supply chain constraints on transformers complicate infrastructure expansion. At the same time, additional pressures from building electrification and other load growth are compounding the challenge.
Being proactive is key to reduce or delay the need for such investments, including EV time-of-use rates, active managed charging programs, and other load shifting and shaping interventions. In addition, when machine-learning (ML) and artificial intelligence are applied to AMI data to reveal territory-wide insights, project managers are able to develop strategies to use EVs for load flexibility and virtual power plants (VPPs). AI also empowers utilities to identify areas where EVs are contributing to non-peak time congestion on the distribution grid.
“The rapid growth in EV adoption creates both challenges and opportunities for utilities,” says Brittany Blair, Manager, Research & Industry at the Smart Electric Power Alliance (SEPA) and one of the authors of SEPA’s recent Insight Brief: AI for Transportation Electrification. “Without proactive management, grid impacts from unmanaged charging could be costly, but with the right data-driven approach, utilities can turn EV charging into a grid asset. And the time to prepare for that future is now.”
*California Public Advocates Office (2023); Kevala (2023); NYSERDA (2022)
U.S. electric utilities have already begun preparing for transportation electrification. As of 2022, 59% of electric utilities in SEPA’s network had established strategic plans for managing this new load, and a key part of those plans is increasing situational awareness of the grid edge and creating data-driven approaches to managing EV charging. What has changed more
recently, is the ability of artificial intelligence (AI) and machine learning to capitalize on existing data flows from the grid and provide the insights needed to enhance utility strategies. At its core, the benefit of using AI for EV detection is precisely that: more sophisticated, data-driven situational awareness.
Artificial intelligence and machine learning provide utilities with an advanced understanding of EV impacts and allow them to better plan for EV demand growth. Using AI, forward-thinking utilities are accelerating their efforts to identify EV-driving customers, create targeted marketing and EV engagement programs, and account for EV charging within their broader distribution system management strategies.
According to SEPA’s research, effectively managing transportation electrification is made easier with a four-step, AI-enabled process:
For transportation electrification specifically, AI solutions that detect EVs from premise-level meter data give utilities visibility into charging behaviors without requiring additional hardware investments. Traditional methods of gathering EV ownership information like vehicle registration records, telematics and customer surveys can leave a data gap that limits utilities’ ability to plan distribution systems efficiently and implement effective EV load management programs. AI can reveal what traditional methods miss.
The Insight Brief: AI for Transportation Electrification features two case stories from utilities who are leading the way when it comes to AI-informed EV strategies: Hydro One in Ontario, Canada, and NV Energy in the state of Nevada.
With EV adoption growing, particularly in areas already facing load growth from new construction, Hydro One was seeking better ways to identify EV drivers beyond customer surveys. By implementing Bidgely’s AI-powered analytics, Hydro One was able to identify 20,000 customers with EV charging activity — approximately 10 times more than they had identified via customer surveys. The utility also realized what it calls its “highest click through rate” in recent history via targeted email recruitment campaigns and is able to create territory-specific EV load shapes to inform grid planning.
NV Energy turned to AI to better understand customer preferences and charging trends while identifying and testing technology that could help improve distribution and resource planning processes to prepare for future grid constraints from additional EV load. Using Bidgely’s patented AI, NV Energy was able to detect 50 customers with high-value baseline charging behavior and achieve a load-shift potential of 2-4 kW/vehicle per event – far above the typical 0.2-0.8 kW/vehicle. The utility’s learnings helped inform its 2025-2027 Transportation Electrification Plan.
As utilities advance their distribution system planning capabilities for the future grid, improved EV insights can inform a cascade of other investments. Utilities can deploy AI’s capabilities in classification, assessment, automation, prediction, and customer engagement benefitting their teams at every stage from strategic planning to system investments to operations.
“Utilities that invest in managed charging strategies now will have an advantage in navigating the transportation electrification transition before EV adoption reaches a critical point in their territories,” emphasizes Blair. “As EV adoption continues to accelerate, the value of having a utility strategy based on precise, granular charging distribution and load patterns will aid in having a more flexible grid, more targeted infrastructure investments, and overall better customer experience. Data and AI capabilities are one piece of that strategy.”
To read the full utility case stories and learn more about how you can leverage AI to prepare for the growing adoption of electric vehicles, maintain grid reliability and grow customer satisfaction download the full Insight Brief: AI for Transportation Electrification today. And, reach out to our team to schedule a live demo of Bidgely’s EV Solution.
