Jeff Perkins, ERS, for Zondits
ACEEE held their third Intelligent Efficiency Conference in Austin from Dec 4‒6. This event is designed to focus on how information and communication technologies (ICT) have transformed our economy and revolutionized the relationship between economic production and energy consumption.
I was on a panel that discussed how electric, gas, and water utilities might align their goals. Specifically, I was asked to take a perspective from the gas side and to discuss how and what data might be shared for common or mutually beneficial purposes. On the same panel, EnergySavvy presented information on how programs can share implementation approaches, and Dropcountr covered the water utility approach to water efficiency goals.
You can use the slides below to follow along with the summary of my presentation.
The obvious starting point for a data sharing discussion is the fact that all three utility services are actively engaged in installing advanced meter infrastructures (AMIs). While examples of municipal utilities sharing AMI systems through common platforms can be found, by and large the majority of these efforts across the country are independent of each other.
AMI is one thing, but since this conference takes a broad view of ICT I hasten to point out that more specific and granular third-party data infrastructures are also expanding. For this reason, we need not limit the conversation to AMI data, which carries with it certain burdens and complications that independent networks like Nest and others might avoid. Before delving into the data, though, I took a look at where gas and electric utilities might find commonality in their goals.
In slides 6 through 17 I lay out two areas for alignment of objectives: combined heat and power (CHP) and demand response (DR). We all know that successful CHP requires more than technical feasibility and even more than economic viability. Using a proactive approach to influence when and where distributed generation occurs, utilities could work together to identify the most “desirable” locations for CHP. Using data on gas consumption as a proxy for available thermal load combined with data on electric grid constrained areas would yield ideal locations to target customer engagement.
While that example could rely on existing utility data infrastructure, alternative data sources will become increasingly available. A convenient example of this is gas DR. While this concept is new and may be unique to the Northeast due to gas delivery constraints (as mentioned in the slides), it is interesting for illustration purposes. While AMI data may be more sophisticated, a Wi-Fi thermostat network might be a more expeditious means to engage DR prospects.
For sure there are places where interests align and working together will be helpful. In the final group of slides, 18 through 22, I bring all of this back to the theme of the conference. It is important that as we consider data from metering infrastructures that we consider the broader ICT trends and not limit ourselves to AMI. While AMI is more granular in time, it remains a whole-building solution and lacks granularity on occupants and leaves us guessing as to equipment and other details. That is where third-party infrastructures can enhance our view and better inform our decisions. Historically, our industry has taken a “functional focus” toward data. From electric meters to M&V submetering, we gather only what we need and only when we need it. Increasingly, systems have a “data focus,” gathering vast amounts of data and then managing a process to anonymize and deliver value, that oftentimes was unforeseen at the outset. Moving forward, our industry needs to combine both, moving as much data as possible from buildings, to the edge and onward to the cloud where we can derive higher value from it.