The flywheel effect represents a powerful business model where multiple interconnected actions create a positive feedback loop, driving continuous, accelerating growth.
Amazon’s meteoric rise to a $2.5 trillion market cap exemplifies this concept perfectly. As customer numbers grew on their platform, more sellers and products were attracted to join, which in turn drew even more customers — creating an ever-accelerating cycle of value and growth.
Not unlike the pre-Amazon retail industry, legacy utility operations for power generation, distribution, and customer engagement have traditionally followed a linear value chain. Grid operations and customer interactions have seldom functioned as an integrated system. Today, however, this conventional paradigm faces unprecedented challenges as increasing adoption of electric vehicles, renewable energy sources, and battery storage systems enable consumers to actively participate in grid management.
The energy landscape is evolving as billions of decisions made by hundreds of millions of stakeholders — utilities, customers, regulators, and others — reshape how energy is produced, stored, managed, and consumed. Utility operations are developing into connected energy platforms that mirror Amazon’s integrated ecosystem.
And just as the retail sector was disrupted by the flywheel effect, the utility industry’s flywheel has begun to turn and is gaining momentum. Let’s explore how this new paradigm is taking shape to create the resilient grid of the future.
Customer Engagement: Setting the Flywheel in Motion
The utility flywheel begins with meaningful customer engagement.
As EVs, renewables, and other distributed energy resources (DERs) gain widespread adoption, we’re seeing significant variations in both their geographical distribution and DER types. Data indicates that even neighboring zipcodes may see as high as a tenfold difference in EV ownership. Likewise, customers may choose to install an L1 or L2 charger depending on their charging needs.
These variations make it clear that treating customers region-wide as a single uniform cohort is no longer effective. To truly resonate with customers, utilities must employ messaging and engagement strategies tailored to each customer’s unique appliance usage, EV/solar DER ownership, and lifestyle preferences.
Fortunately, by leveraging Bidgely’s UtilityAI™ Platform, utilities can now detect which customers have EVs, solar installations, or inefficient appliances, while also defining each customer’s energy use patterns across these devices. This allows utilities to hyper-personalize messaging with relevant insights and calls to action. For EV owners, for instance, messaging can be customized based on charger type, charging patterns, and whether they’re enrolled in time-of-use (TOU) rate plans.
This is how the flywheel is set in motion: by delivering exceptional, personalized customer experiences that foster positive sentiment toward the utility and motivate active participation in supporting the grid.
Grid Planning: Leveraging Data for Intelligent Growth
The second part of the flywheel focuses on intelligence-driven grid planning.
Behind-the-meter (BTM) customer energy use data provides utility grid analysts with critical insights into where grid assets face constraints — identifying whether distribution transformers, substations or feeders are experiencing peak-time overload and understanding the root causes. As mentioned earlier, these BTM insights are especially valuable at both the home and circuit level due to the significant geographical and device-type variations in DER adoption patterns.
Bidgely’s AI-enabled platform helps identify which customers own solar, EVs, and/or batteries, the type of EV chargers they use, and their charging patterns. UtilityAI™ provides a granular understanding of appliance-level energy consumption patterns for every individual customer on an hourly, daily and monthly basis to help utilities pinpoint those customers who have the greatest load shaping or shifting potential to alleviate grid constraints.
Bottom-up load aggregation of these “disaggregation-based BTM insights” further empowers utility teams to proactively identify where grid assets — transformers, feeders or substations — are likely to become stressed in the future.
Load Management: Optimizing for Resilience
This brings us to the last part of the flywheel: load management.
Efforts to manage peak load through demand response can take multiple forms: shimmying, shedding, shifting, and shaping. Among these, load shifting and load shaping prove most effective for addressing long-term grid stability as DERs scale.
With Bidgely’s AI-based disaggregation of BTM data, utilities not only gain access to energy usage by appliance, they can also identify specific appliance types — including EVs, and gas vs. electric appliances — as well as consumption patterns that reveal whether an individual home consistently uses energy during peak hours or exhibits off-peak, low-load tendencies. Thus, utilities are able to better target program recruitment to the ideal customers for load shaping or shifting.
The synergistic benefit here is clear: because utilities have already leveraged disaggregation to deliver exceptional, hyper-personalized customer experiences, these target customers are primed to become willing participants in demand response programs.
Participating in such programs reduces overall energy footprint and costs for customers, thereby further enhancing customer experience and bringing us full circle to the beginning of the flywheel, where it continues to turn with increasing momentum.
Unleash the Power of the Energy Flywheel
Just as Amazon’s flywheel inspired a retail evolution, the energy space is now poised for its own transformative flywheel transition.
If you would like to learn more about how an integrated approach to customer engagement, grid planning and load management can accelerate growth and resilience in your organization, reach out to our team to schedule a live demo.
