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In this study a detailed model of a single-family house with an exhaust air heat pump and photovoltaic system is developed in the simulation software TRNSYS. The model is used to evaluate three control algorithms using thermal and electrical storage in terms of final energy, solar fraction, self-consumption and seasonal performance factor. The algorithms are tested and compared with respect to energetic improvement for 1) use of the heat pump plus storage tank for domestic hot water and space heating, 2) use of the electrical storage in batteries and 3) use of both electrical and thermal storage. Results show the highest increase of self-consumption to 50.5%, solar fraction to 40.6% and final energy decrease to 6923 kWh by implementing the third algorithm in a system with 9.36 kW PV capacity and battery storage of 10.8 kWh. The use of electrical energy storage has higher positive impact compared to the thermal storage with the settings and component sizes used. The combined use of thermal storage and batteries leads to final energy savings that are nearly the same as the combined savings of thermal storage and batteries separately, showing that they are mostly independent of one another for the settings of this study.

Operational control strategies for the heating system and “smart” utilization of energy storage were developed and analyzed in a simulation based case study of a single-family house with exhaust air heat pump and photovoltaic system. Rule based control algorithms that can easily be implemented into modern heat pump controllers were developed with the aim to minimize final energy and maximize self-consumption by the use of the thermal storage of the building, the hot water tank and electrical storage. Short-term weather and electricity price forecasts are used in some of the algorithms. Heat supply from an exhaust air heat pump is limited by the ventilation flow rate fixed by building codes, and compact systems employ an electric heater as backup for both space heating and hot water. This heater plays an important role in the energy balance of the system. A typical system designed for new detached houses in Sweden was chosen for the study. This system, together with an independent photovoltaic system, was used as a base case and all results are compared to those for this base case system. TRNSYS 17 was used to model the building and system as well as the control algorithms, and special care was taken to model the use of the backup electric heater as this impacts significantly on final energy use. Results show that the developed algorithms can reduce final energy by 5–31% and the annual net cost for the end user by 3–26%, with the larger values being for systems with a battery storage. Moreover, the annual use of the backup electric heater can be decreased by 13–30% using the carefully designed algorithms.

Operational control strategies for the heating system of a single-family house with exhaust air heat pump and photovoltaic system and “smart” utilization of energy storage have been developed and evaluated in a simulation study. The main aim and novelty of this study is to evaluate the impact on the benefit of these advanced control strategies in terms of performance (energy use and economic) for a wide range of boundary conditions (country/climate, occupancy and appliance loads). Short-term weather data and historic price data for the same year as well as stochastic occupancy profiles that include the domestic hot water load are used as boundary for a parametric simulation study for the system modeled in detail in TRNSYS 17. Results show that the control using a forecast of dynamic electricity price leads to greater final energy savings than those due to the control using thermal storage for excess PV production in all of the examined locations except Sweden. The impact on self-consumption using thermal storage of heat produced by the heat pump using excess PV production is found to decrease linearly with increasing household electricity for all locations. A reduction in final energy of up to 842 kWh year−1 can be achieved just by the use of these algorithms. The net energy cost for the end-user follows the same trend as for final energy and can result in cost savings up to 175 € year−1 in Germany and Spain due to the use of the advanced control.