Comfort-Bounded Heat Pump Flexibility for Electrified Buildings
Heat pumps are central to building decarbonization and can also become flexible grid resources, but treating residential heating and cooling as raw curtailment risks undermining comfort, participation, and reliability. This paper synthesizes official energy-system sources, demand-flexibility program reports, building-code guidance, thermal comfort standards context, and heat-pump field studies. The evidence shows a consistent pattern: grid-interactive efficient buildings are defined around co-optimization of energy cost, grid services, and occupant needs; heat-pump deployment can raise winter peaks unless paired with efficiency and demand-side management; and field studies show that event duration, preconditioning, opt-outs, weatherization, and delayed indoor-temperature response shape delivered savings. The contribution is a comfort-bounded flexibility stack that separates efficiency, thermal storage, device communication, occupant override, grid dispatch, and measurement. The conclusion is that scalable heat-pump flexibility should be designed as constrained service delivery, not as invisible load shedding.
Introduction
Building electrification changes the shape of electricity demand. Heat pumps can reduce direct fossil-fuel use and improve efficiency, but they also move space-heating load onto electric distribution systems. The U.S. building decarbonization blueprint record states that buildings account for 74 percent of U.S. electricity use and that building heating and cooling drive peak electricity demand [[cite:osti_blueprint]]. DOE also frames building-grid interaction as one of four strategic objectives for reducing building-sector emissions [[cite:doe_blueprint_release]].
Heat pumps sit directly inside that transition. IEA states that accelerated heat pump deployment increases electricity demand, although efficiency and demand response can reduce power-system impacts [[cite:iea_future_heat_pumps]]. DOE describes heat pumps as transferring heat rather than generating it, and notes that current air-source heat pumps can reduce heating electricity use by up to 75 percent compared with electric resistance heating [[cite:doe_heat_pump_systems]]. IEA also reports that in 2023 heat pumps could meet more than 60 percent of global space and water heating demand with lower operational CO2 emissions than condensing gas boilers [[cite:iea_heat_pumps_tracking]].
The opportunity is not just efficient heat. Grid-interactive efficient buildings combine efficiency, demand flexibility, controls, and communication to serve the grid while meeting occupant needs. The DOE Roadmap sets a goal to triple building energy efficiency and demand flexibility by 2030 relative to 2020 levels [[cite:doe_geb_roadmap,doe_geb_energygov]]. Connected Communities material defines a GEB as an energy-efficient building using smart equipment or distributed resources to provide demand flexibility while co-optimizing energy cost, grid services, and occupant needs and preferences, and DOE announced connected-building pilots covering more than 7,000 buildings [[cite:connected_communities_geb,doe_connected_communities]].
This paper argues that heat-pump flexibility must be comfort-bounded. Residential HVAC differs from batteries because its storage medium is the occupied building and its failure mode is felt by people. NEEA states that residential HVAC demand response is constrained by time-dependent thermal comfort and is often needed at the very times when the grid most needs demand reductions: summer and winter extremes [[cite:neea_res_hvac_dr]]. The research question is: how should heat-pump demand flexibility be framed so that grid value does not erase occupant comfort, participation, or equity?
The contribution is a comfort-bounded flexibility stack. It treats demand response not as raw curtailment, but as constrained service delivery across six layers: envelope and equipment efficiency, preconditioning and thermal storage, device communication, occupant override, grid dispatch, and measurement. Each layer can add flexibility, but each also has a comfort or reliability boundary.
Method
I used a conceptual-synthesis method grounded in official sources, technical reports, peer-reviewed or conference studies, and public program documents. AlexandrAI graph search found no direct archive duplicate for heat-pump demand response, grid-interactive efficient buildings, thermal comfort flexibility, smart thermostat heat pumps, residential load flexibility, or building electrification grid topics. External searches prioritized DOE, IEA, EIA, FERC, LBNL, PNNL, ENERGY STAR, ASHRAE/ANSI context, and open heat-pump field studies.
Sources were screened for three roles. First, system-scale sources established why heat pumps and buildings matter for electrification, demand flexibility, and peak load [[cite:doe_blueprint_release,osti_blueprint,eia_homes_energy,eia_heating_electricity_2025]]. Second, GEB and demand-response sources defined the control objective and program context [[cite:doe_geb_roadmap,naseo_geb101,pnnl_codes_geb,ferc_2025_assessment]]. Third, comfort and heat-pump field sources established limits, opt-outs, preconditioning, and customer experience [[cite:osti_comfort_participation,doe_heat_pump_load_flex,aceee_cordova_load_flex,neea_res_hvac_dr]].
The synthesis coded each full-read source for one of six layers: efficiency baseline, thermal storage or preconditioning, device communication, occupant boundary, grid dispatch, and measurement. Claims about numbers were kept source-specific. For example, FERC retail potential peak demand savings are not treated as heat-pump-specific savings; Cordova heat-pump field results are not generalized as national averages.
usable flexibility = grid response - comfort violation risk - rebound risk - participation attrition
Equation (1) is a conceptual lens rather than a measured formula. It states the central inference: a dispatch event that produces a short load drop can still be a poor flexibility resource if it creates discomfort, snapback, opt-outs, or long-term customer withdrawal. The paper therefore evaluates heat-pump flexibility as a service-quality problem as well as a load-shape problem.
System Context
Finding 1: heat pumps expand a load class that already shapes the grid. EIA reports that in 2020 electricity and natural gas each supplied a little over two-fifths of U.S. residential end-use energy, and that about 13 percent of households used a heat pump as main heating equipment [[cite:eia_homes_energy]]. In 2024, 42 percent of U.S. households reported electricity as their main space-heating fuel, while 47 percent reported natural gas [[cite:eia_heating_electricity_2025]]. Not all electric heating is heat-pump heating, but the direction is clear: residential heating is becoming more electric.
IEA clarifies the peak-load risk. A household adding a heat pump without parallel efficiency improvements can nearly triple winter peak demand; a two-grade efficiency improvement can halve heating energy demand and reduce peak growth by one third [[cite:iea_future_heat_pumps]]. This is the first boundary of the stack: flexibility should not be asked to compensate for avoidable envelope and equipment inefficiency.
Finding 2: demand response is already large, but it is not automatically heat-pump flexibility. FERC reports 30,542 MW of U.S. retail potential peak demand savings in 2023, with residential, commercial, and industrial classes contributing 31.7, 23.0, and 45.3 percent, respectively [[cite:ferc_2025_assessment]]. Figure 1 visualizes the class split. This establishes the scale of demand response as a grid resource, but it also shows why heat-pump claims need narrower evidence.
Program evidence points in the same direction. A Berkeley Lab ACEEE paper reports that more than 11.6 million U.S. electricity customers provided about 29.5 GW of retail demand-flexibility capacity in 2020, and it found Wi-Fi thermostat programs in 33 states [[cite:aceee_programs_rates]]. Yet that same paper emphasizes data gaps in enrollment, participation, energy outcomes, and rate impacts. A comfort-bounded stack treats program reporting as a starting point, not proof that heat pumps are a dependable winter resource in every climate.
Comfort Boundary
Finding 3: demand flexibility definitions already include service quality. NASEO defines demand flexibility as the ability of buildings and equipment to adjust energy use dynamically in response to grid conditions, while GEBs co-optimize occupants and the grid [[cite:naseo_geb101]]. PNNL code guidance describes demand flexibility as shifting electricity use across hours while delivering end-use services at the same or better quality and lower cost [[cite:pnnl_codes_geb]]. These definitions reject the idea that occupant discomfort is an acceptable hidden cost.
Thermal comfort cannot be reduced to a thermostat setpoint. ANSI summarizes ASHRAE 55 as addressing metabolic rate, clothing insulation, air temperature, radiant temperature, air speed, and humidity to produce conditions acceptable to a majority of occupants [[cite:ashrae_55_ansi_blog]]. That standard context is important because a heat-pump event can change temperature while leaving other comfort dimensions and occupant vulnerability unmeasured.
Field evidence makes the comfort boundary operational. The OSTI record for a residential demand-flexibility study reports that real-world understanding of thermal comfort during space-conditioning events remains limited, and that a Cordova, Alaska heat-pump field study found event duration more important than offset size, delayed indoor-temperature response important to comfort perception, and 18 to 22 C operative temperatures potentially preferred in that setting [[cite:osti_comfort_participation]]. DOE peer-review material likewise states that few studies exist on residential winter demand flexibility with heat pumps and that heat-pump demand response may affect comfort and daily routines [[cite:doe_heat_pump_load_flex]].
The Cordova ACEEE field paper gives concrete event results. It reports average daily heat-pump energy use of 39 kWh and baseline event-hour energy use of 1.7 kWh. Excluding opt-out events, one-hour events produced a 12 percent hourly reduction, two-hour events 24 percent, and two-hour events with preheating 35 percent. The same paper reports a 19 percent thermostat override rate and notes supplemental behaviors such as using wood stoves on some event days [[cite:aceee_cordova_load_flex]].
NEEA identifies the mechanisms behind those results: pre-conditioning can reduce customer impact, snapback can create post-event load, opt-outs can reduce participation, and weatherization increases the useful thermal storage of the building envelope [[cite:neea_res_hvac_dr]]. BPA similarly describes smart thermostats as able to pre-cool or pre-heat homes and use weather forecasts to adjust for peak demand [[cite:bpa_residential_dr]]. The practical lesson is that flexibility is not a single event parameter; it is a sequence of preparation, event, recovery, and continued participation.
Comfort-Bounded Flexibility Stack
The synthesis supports a six-layer stack. Layer 1 is efficiency: insulation, air sealing, weatherization, right-sized equipment, and efficient heat-pump operation. Layer 2 is thermal preparation: preheating, precooling, and passive or active thermal storage. Layer 3 is device communication: smart thermostats, connected heat pumps, and standards that let the grid request rather than manually force a response. Layer 4 is the occupant boundary: overrides, opt-outs, preferences, health limits, and adaptive behavior. Layer 5 is dispatch: event timing, duration, magnitude, and rebound management. Layer 6 is measurement: delivered demand savings, comfort outcomes, opt-out rates, and persistence across seasons.
ENERGY STAR helps locate a communication boundary. Certified smart thermostats must be able to work with utility demand response programs, but ENERGY STAR does not require specific responses and does not require the function to be active in every installation [[cite:energystar_smart_thermostats]]. Therefore certification compatibility should not be interpreted as guaranteed dispatch performance. It is a precondition for program design, not a result.
Power-sector modeling reinforces why thermal storage belongs in the stack. A 2024 Communications Earth & Environment study of Germany 2030 scenarios found that short-duration heat storage can reduce the need for firm capacity and battery storage, and that 2-hour heat storage can shift heat-pump electricity use toward midday solar and smooth morning peaks [[cite:nature_flexible_heat_pumps]]. The same article cautions that larger storage operating patterns may be less realistic for small decentralized heat pumps. The layer therefore asks for right-sized flexibility, not maximum shift at any cost.
Figure 3 is conceptual, but it follows the field-study pattern: duration can matter more than offset, preconditioning can improve event performance, and delayed temperature response can influence willingness to keep participating [[cite:osti_comfort_participation,aceee_cordova_load_flex]]. The purpose is not to set a universal limit. It is to show that a program needs a comfort constraint function, not only a kW target.
Discussion
The core answer to the research question is that heat-pump flexibility should be procured as bounded thermal service. A utility or aggregator may need a load reduction at a feeder peak, but the customer receives a service: safe and acceptable indoor conditions. GEB definitions, PNNL code guidance, and LBNL demand-flexibility work all point toward co-optimization rather than unilateral curtailment [[cite:connected_communities_geb,pnnl_codes_geb,lbl_demand_flexibility]].
This changes program design. A heat-pump demand-response program should report kW reduction, event duration, rebound, opt-out rate, indoor temperature or comfort proxy, vulnerable-household protections, and persistence across seasons. ACEEE program evidence shows that program and rate outcome data are not yet standardized enough for easy cross-program comparison [[cite:aceee_programs_rates]]. FERC likewise reports national demand-response potential while noting reliance on public data and limitations in verification [[cite:ferc_2025_assessment]]. A comfort-bounded stack would make those limitations visible rather than treating aggregate MW as proof of dependable flexibility.
The model also changes technology procurement. Device compatibility is necessary, but the dispatch contract should specify acceptable event types, communication failure behavior, customer notification, override handling, and recovery control. ENERGY STAR compatibility alone does not define the event response [[cite:energystar_smart_thermostats]]. BPA examples of preheating, precooling, and weather-aware adjustment illustrate the kind of control logic that can be made explicit [[cite:bpa_residential_dr]].
Equity is not an optional add-on. DOE notes that one in five Americans lives in a household at least one month behind on energy bills and that disadvantaged communities face energy insecurity and substandard building conditions [[cite:doe_blueprint_release]]. IEA notes that appropriate support is needed for poorer households to manage heat-pump upfront costs and realize bill savings [[cite:iea_future_heat_pumps]]. If programs recruit customers with poor envelopes into aggressive events, they may turn grid flexibility into discomfort for the households least able to refuse incentives.
The paper has limits. It is a conceptual synthesis, not a new field trial. Several load-reduction numbers come from a rural cold-climate field study and are reported with opt-out exclusions [[cite:aceee_cordova_load_flex]]. Comfort standards define conditions acceptable to a majority, not every person or health circumstance [[cite:ashrae_55_ansi_blog]]. Heat-pump emissions benefits depend on grid mix, refrigerant management, installed performance, and policy context [[cite:iea_heat_pumps_tracking]]. Power-sector benefits modeled for Germany do not automatically transfer to U.S. feeders or individual homes [[cite:nature_flexible_heat_pumps]].
Future work should connect device telemetry, indoor condition data, feeder constraints, and participation records in privacy-preserving evaluations. The most useful study would compare event strategies across weatherized and non-weatherized homes, different heat-pump types, and different climates while reporting kW reduction, snapback, comfort proxy, opt-out behavior, and persistence. Only then can heat-pump flexibility move from plausible resource to bankable grid service.
Conclusion
Heat pumps can be efficient building-decarbonization devices and flexible grid resources, but the same occupied space that creates thermal storage also creates a hard boundary: people must remain comfortable and able to participate voluntarily. Treating residential heat-pump load as generic curtailment misses that boundary.
The comfort-bounded flexibility stack makes the boundary explicit. First reduce avoidable load through efficiency. Then use preconditioning and thermal storage carefully. Ensure connected equipment can respond to grid signals. Preserve override and opt-out. Dispatch events according to local climate, building stock, and feeder need. Measure savings, comfort, rebound, and persistence together.
The practical rule is simple: do not count heat-pump flexibility unless the program can also count the service quality that made the flexibility acceptable.