With a warming climate and a rapidly evolving energy system, energy grids are increasingly struggling to meet the demands placed on them. Many utilities have successfully implemented demand response (DR) programs to help manage periods of particularly high demand, but these efforts will likely need to be scaled dramatically on both the electric and gas systems to ensure reliability going forward. To engage and motivate as many customers as possible, utilities and regulators should consider a shift to a performance-based approach to load flexibility that’s enabled by real-time data collection and cloud-based computation.
Just last month, the Electric Reliability Council of Texas (ERCOT) asked customers to voluntarily conserve energy for eight days in a two-week period to avoid rolling blackouts. Outside of Texas, a range of other grid operators have also asked customers to voluntarily conserve electricity to help ensure reliability in both the summer and winter over the past year. Nationally, data from the U.S. Energy Information Administration suggest that power outages are occurring more often and lasting longer, with average outage durations doubling between 2013 and 2021. And it’s not just electricity: in areas like the Northeast US where policies are leading to capacity constraints on the gas system, load management may be an increasingly essential non-pipe alternative that can help avoid gas shortfalls in extreme winter storms.
At present, most utility DR programs on the residential side involve technologies like smart thermostats that can be remotely controlled to reduce peak loads, often with a $20-25 annual incentive for customers that participate in a minimum number of events. However, that approach is inherently limited given that roughly 80% of customers don’t yet have a smart thermostat installed, some customers (particularly lower-income customers or those in disadvantaged communities) may not have strong Wi-Fi networks to enable automation, and load reductions are generally estimated using standardized assumptions rather than measured directly.
One way to overcome these challenges and help deliver more benefits at scale could be to take a performance-based approach to demand response with dynamic incentives tied either to the real-time wholesale price of power or the estimated cost of power outages or gas shortfalls. By providing incentives based on measured load reductions—rather than estimates based on equipment runtime data—utilities could expand customer choice and improve equity while also getting better, more precise data on actual electric or gas load reductions.
Customers could also stand to benefit through larger incentives in a performance-based model. For instance, real-time wholesale prices of power have risen to more than $1 per kilowatt-hour (kWh) in multiple regions of the US at times of high demand, and rose to roughly $4/kWh on five different days in August this year in Texas. If similar levels of incentives were passed through to customers as an incentive for avoided consumption, potential bill savings could far exceed the standard single $25 annual incentive most DR programs provide while enabling many more customers to participate.
To make this kind of performance-based model especially effective, utilities should have:
- High-resolution consumption data (ideally more frequent than 15-minute intervals) to ensure accurate load impact measurements.
- The ability for rapid cloud-based measurement and verification (M&V) using standardized methodologies to streamline incentive calculations.
- A data processing and analysis strategy that doesn’t require extensive additional in-house resources.
- Real-time reporting to customers to drive engagement, increase transparency, and help them understand what’s working and what’s less effective during an event.
However, even the newest smart meters aren’t well suited to deliver this kind of performance. Not only is data transmission to the utility often delayed by at least a full day—making rapid data analysis and customer communications nearly impossible—but manufacturers’ growing focus on edge computation in the meter itself may limit utilities’ ability to explore more sophisticated and precise methodologies for real-time M&V that could be enabled through a cloud-based system. And, since data analysis typically falls to the utility or their program implementers with current approaches, it may be slower, more labor-intensive, and more expensive than necessary. Fortunately, new options from companies like Copper Labs can deliver near-real-time data from existing meters—including both smart meters and older drive-by AMR systems—to utilities and customers while supporting timely data analysis through a streamlined cloud-based approach.
With a growing range of challenges to the electric and gas systems, expanded demand response and load flexibility offerings have the potential to help significantly improve reliability. By pairing innovative technologies with new incentive frameworks, utilities and regulators have a unique opportunity to deliver deeper benefits to both customers and the grid while also supporting an evolving and decarbonizing energy system.
