Report finds significantly less competition between these resources than might be assumed, encouraging co-deployment in virtually any combination
Demand flexibility is growing in importance to the grid, as more states and regions ramp up variable renewable energy generation and retire firm capacity. With energy efficiency (EE) and demand response (DR) poised to play an increasingly key role on future grids, Berkeley Lab conducted a four-part, multi-year study looking at the interactive effects of EE and DR from both the building owner and power system perspectives.
The latest study, Assessing the Interactive Impacts of Energy Efficiency and Demand Response on Power System Costs and Emissions, finds that contrary to intuition, EE and DR largely complement each other to reduce power system costs and emissions. And while it sheds important light on optimal EE and DR combinations, the study also suggests that utilities and grid operators can successfully deploy these resources in virtually any pairing.
Building on prior series findings, the new study quantifies how EE and DR – in isolation, and in combination – impact power system costs and carbon emissions across three grid scenarios: a mid-case, plus limited transmission and high renewables scenarios. It approximates findings for the contiguous U.S. but focuses on the Texas grid because its relative isolation is advantageous for studying interactions between different types of EE and DR.
The study modeled three kinds of EE packages:
- Controls measures (e.g., residential smart thermostats, networked lighting controls, and commercial energy management systems)
- Envelope measures (e.g., improved insulation and windows)
- Equipment measures (e.g., upgraded HVAC equipment and appliances)
It also considered DR technologies that reduce peak load (“Shed DR”) or shift it from peak to off-peak times (“Shift DR”).
The figure below summarizes the study’s findings on how EE and DR combine to lower power system costs in the Electric Reliability Council of Texas (ERCOT) through 2040. In all cases, costs are reduced when DR technologies are added to EE packages:
The study also found that both Shed DR and Shift DR lower power system emissions in ERCOT compared to EE packages in isolation. Notably, the study did not include a price on carbon, which would have driven further emissions reductions.
Some EE and DR combinations seem particularly advantageous. For example, the study’s ERCOT modeling found that:
- Controls EE pairs well with either form of DR from a cost and capacity perspective. In a Mid Case scenario, Controls EE + Shed DR enhanced cost savings, while Controls EE + Shift DR enabled more energy exports.
- Envelope EE + Shift DR consistently reduced power generation costs.
- Shift DR + any EE package reduced emissions in all scenarios, as did Shed DR + Controls EE or Equipment EE.
Other findings suggest that grid characteristics are important drivers of EE and DR interactions. For example, Envelope EE + Shed DR increased net cost savings in the reference “Mid Case” scenario by roughly 20x compared to Shed DR alone, a magnitude not observed in other scenarios. In a limited transmission scenario (i.e., where local generation is more heavily utilized due to very high costs of new transmission), EE is much more complementary with Shift DR than with Shed DR.
The study did find that Equipment EE measures generally compete with DR to provide system cost savings, because Equipment EE substantially reduces the DR resource size and therefore the potential cost and capacity savings from DR. Even so, Equipment EE + Shift DR led to emissions benefits in the form of reduced coal and gas capacity and more wind and solar capacity in all ERCOT scenarios.
The study’s publication concludes several years of advanced Berkeley Lab research into quantifying EE and DR interactions at both the building and power system levels. The series’ first report, published in 2020, introduced a conceptual framework outlining the various attributes, system conditions, and technological factors driving these interactions. The second study, published in Advances in Applied Energy, explored load-driven interactions between EE and DR on regional grid scales, while the third, published in Smart Energy, developed metrics to characterize the power system’s need for DR resources.
The research utilized publicly available U.S. Department of Energy tools, including NREL’s Regional Energy Deployment System (ReEDS) capacity planning model and the ResStock and ComStock models of U.S. residential and commercial buildings, as well as Berkeley Lab’s DR-Path model, which estimates the available DR resource according to building type and energy end-use.
The new study’s methodology enabled researchers to capture, for the first time, dynamic interactions between EE and DR within the context of capacity expansion modeling, allowing future studies and local and regional grid planning efforts to better understand interactions among energy efficiency, demand flexibility, and distributed energy resources.