Policymakers, regulators and utilities are increasingly prioritizing energy decarbonization in addition to reliability and affordability, and several states—including Colorado, Massachusetts, Minnesota and Vermont—have adopted clean heat standards or frameworks designed to encourage gas and dual-fuel utilities to reduce greenhouse gas emissions. In order to achieve those reductions while simultaneously maximizing affordability, reliability and equity, gas and dual-fuel utilities need better, more granular and more cost-effective sources of gas consumption data that can improve decarbonization planning and implementation while unlocking new non-pipes alternatives (NPAs).
An immediate area where better data can help is in strategic decarbonization planning. For example, where building space heating is concerned, one could potentially replace existing gas systems with all-electric heat pump systems (full fuel switching), hybrid systems that combine an electric heat pump with a gas furnace (partial electrification), or a new more-efficient gas heat pump system (increased efficiency). Each of those decisions can have marked impacts on gas and electric peak demand, overall measure cost-effectiveness, and customers’ overall energy bills. But without high-interval data on customers’ usage—particularly during very cold days—utilities and other stakeholders could make flawed assumptions about the impacts associated with different options that ultimately lead to decisions that stress the grid by driving unexpectedly high new peaks in demand, unnecessarily inflate gas or electric prices, or exacerbate existing inequities in vulnerable communities.
High-resolution data can also help drive deeper, more cost-effective reductions in gas use—something that will be critical in any gas or dual-fuel utility pathway to significantly reduced emissions. One way this can happen is through improved program targeting, enabling utilities to prioritize customers that will see the biggest benefits from energy efficiency or strategic electrification programs. As an example, when National Grid ran a pilot with Copper Labs to access near-real-time data collected from its existing gas AMR meters without hardware retrofits, the resulting data was granular enough to show furnace runtimes and help identify homes with inefficient heating systems. That type of data could be invaluable in identifying specific customers who could benefit the most from heating system improvements. Better data can also enable improved behavioral efficiency programs by helping customers understand the impacts of their decisions in real time; mid-cycle high bill alerts that could be paired with educational information on utility programs or marketplaces to help deliver useful insights to customers when they are likely to be most receptive; and streamlined measurement and verification efforts.
Finally, improved data can also unlock a range of new NPAs through expanded gas load flexibility that can help ensure reliability and increase resilience in the face of more extreme climate-driven storms or new policies that could make it harder for gas utilities to replace pipes (or even drive gas system decommissioning). For instance, in the pilot mentioned above, National Grid used Copper Labs’ platform to run a behavioral demand response event in the middle of a strong winter bomb cyclone and saw an impressive 18% reduction in consumption from customers who received the messaging compared with those who did not. While load flexibility and demand response will necessarily look different for gas utilities than they will for electric utilities, it’s an area rife with opportunity for increased exploration. Regardless of the approach used to reduce demand, high-interval data can also open the door to new incentive approaches for load flexibility, such as customized real-time incentives that reflect actual customer load impacts and benefits to the utility system for a given event.
Despite the many benefits of granular, near-real-time gas data, many utilities still only have access to monthly gas meter data at best. Gas smart meter adoption lags well behind electric, and even the newest smart meters often have inherent challenges and latencies that can limit their applicability. But there are now a range of other opportunities to get better, high-resolution gas consumption data, and utilities should be exploring all options to understand what will best meet their needs. Improving data collection may not seem as exciting as many other things utilities could be doing, but it’s a critical and urgent need if we are to successfully address the emerging challenges of decarbonization, affordability, equity, reliability and resiliency in the face of climate change.
