Heat Pump Scheduling Model Coordinates Solar and Dynamic Tariffs

A new scheduling model targets the hard part of electrified heating

Researchers at Cranfield University in the United Kingdom have developed a scheduling model for residential heat pumps designed to reduce electricity costs while preserving thermal comfort in homes with rooftop solar.

The work, reported by pv magazine, addresses a practical challenge that is becoming more important as households combine heat pumps, solar panels, and electricity tariffs that change over time. A heat pump can shift some demand away from expensive hours, but only if it can do so without leaving occupants too cold or too warm. Rooftop photovoltaic generation adds another variable because solar output is available at some times and uncertain at others.

The Cranfield model is intended to coordinate three resources at once: grid electricity, rooftop PV generation, and the building’s own thermal flexibility. That means deciding when to draw power from the grid, when to use solar generation directly, and when to rely on stored heat in the building fabric or indoor environment.

Dynamic tariffs create an opening for smarter control

Time-varying tariffs change the economics of heating. Electricity may be cheaper during some periods and more expensive during others, creating an incentive to move flexible loads into lower-cost windows. Heat pumps are a major candidate for this kind of load shifting because they consume electricity and can often preheat a home slightly before prices rise.

Banu Yektin Ekren, the corresponding author, told pv magazine that rooftop PV strengthens the heat pump’s load-shifting potential under dynamic tariffs because it gives the scheduler a low-cost source of electricity beyond the grid. The optimization can coordinate when electricity is cheap, when PV is available, and how much thermal flexibility the building can provide.

This is a more complex problem than simply running a heat pump when solar panels are producing. Solar generation is uncertain, household comfort has limits, and electricity prices may not align neatly with peak PV output. A useful scheduler has to make tradeoffs among those factors rather than optimize for one variable in isolation.

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The model links energy cost with comfort

The reported goal of the model is to minimize electricity costs while maintaining thermal comfort. That pairing is important because aggressive cost reduction can undermine the main purpose of a heating system. If a scheduler turns down heating too far during expensive periods, it may save money while producing unacceptable indoor conditions.

By including building thermal storage, the model treats the home itself as part of the energy system. Walls, floors, indoor air, and household thermal mass can retain heat for a period of time. In practice, that means a home can sometimes be heated ahead of a high-price interval, then coast through part of that interval with reduced heat-pump operation.

When rooftop PV is present, the scheduler can also favor operation during solar production windows. That can reduce reliance on grid electricity, particularly when tariff prices are high. The source article does not provide full technical details in the supplied text, but it describes the system as an optimization approach for residential heat-pump operation under dynamic tariffs and uncertain PV generation.

Why this matters for residential electrification

Heat pumps are central to many building decarbonization strategies because they can provide heating efficiently using electricity rather than on-site combustion. Their widespread adoption, however, changes household demand patterns and can add load to distribution grids during heating periods.

Scheduling systems can help by making heat-pump demand more flexible. If many homes can shift some heating to lower-cost or solar-rich periods while staying comfortable, the result could be lower bills for households and smoother demand for energy systems. The same logic applies to homes that are gradually adding rooftop PV, batteries, or other distributed energy resources.

The research also points to a broader shift in residential energy management. As tariffs become more dynamic and homes add more controllable devices, static rules may become less effective. A household energy controller increasingly needs to respond to prices, weather, generation forecasts, comfort constraints, and equipment behavior.

What remains to be proven

The supplied source text describes the model and its intended benefits, but does not include detailed field-test results or deployment plans. The next questions are therefore practical: how robust the scheduler is under real household behavior, how accurately it handles uncertain PV generation, and how easily it can be integrated with existing heat-pump controls.

Even so, the work highlights a concrete path for making electrified heating more economical. Rather than treating a heat pump as a simple appliance that reacts to a thermostat alone, the model treats it as part of a coordinated home energy system. That coordination may become increasingly valuable as more households face variable prices and generate some of their own electricity.

This article is based on reporting by PV Magazine. Read the original article.