- Disaggregation offers a huge value by lowering risk and improving cost effectiveness of DSM programs, if it can be unlocked
- In a world of increasing distributed energy resources and local grid constraints, grid planning can be made more precise, granular and actionable with disaggregation
- New revenue generation, marketing and customer support are also promising use cases
Leveraging AMI data for utility value
Having thus far relied on disaggregation to personalize customer interactions, utilities have approached Bidgely about using disaggregation to enhance analytics and processes. We have spoken with dozens of utilities and their partners to better understand how these analytics can provide value over the last few months and would like to take this opportunity to reflect on what we have learned. Of all the potential use cases, the ones that generate the most interest can be grouped into Demand Side Management (DSM) program optimization and Grid Analytics.
Demand Side Management program optimization
What if you could audit and survey every home in your territory?
That’s what Bidgely’s disaggregation offers – a way to gather insight into appliance ownership, usage, and efficiency across the entire residential customer base. This approach can improve every stage of the Demand Side Management program lifecycle from potential study, to program design, customer targeting & acquisition, and measurement and verification.
Some structural barriers to the way these programs currently are designed and operate can make this value hard to realize for stakeholders, which will be explored in a future blog post. We will continue to work with innovative utilities, implementers and other stakeholders to make sure the value to ratepayers can be fully realized.
The growth in distributed energy resources such as rooftop solar, electric vehicles, energy storage and direct load control presents utilities with both a threat to grid stability, and an opportunity to operate the grid more reliably and cost-effectively than ever before, if harnessed correctly.
Analyzing meter data to inform utilities which homes have which appliances, the size of those appliances and how they contribute to peak load has a myriad of value creating applications, made more pressing by the growth of DERs:
- Create custom end-use load curves by appliance for more precise load forecasting
- Identify contribution to peak load by appliance, and how much is available to shift or shed for non-wires alternatives and integrated DSM efforts
- Analyzing the effect new rate structures will have on consumption patterns
- Identify homes with a high potential to electrify appliances, and their potential effect on peak load
- Identify Electric Vehicle charging at home, and incentivize owners to shift to off-peak charging
- Quantify the effect of solar production on transformer and substation peak load
As the grid becomes increasingly complex, a traditional survey approach does not provide sufficient information for planning and management. Including meter-based insights offers utilities granular, up to date information across their whole residential customer base.
Any effort that requires insights into how customers are using energy in their homes can benefit from unlocking meter data. New revenue generation opportunities necessitate a deep understanding of the customer, their habits and their needs. Marketing and customer education efforts can better personalize communications and offers to customers. Future use cases that go beyond what we think of as typical utility functions are being explored by innovative energy companies such as Origin Energy. All of these are enhanced or unlocked by meter data analytics.
We feel we are just scratching the surface, and look forward to many more interesting discussions on what insights are possible and what they can be used for.