Dynamic evaluations of heat pump and micro combined heat and power systems using the hardware-in-the-loop approach

Mehrfeld, Philipp; Nürenberg, Markus; Knorr, Martin; Schinke, Lars; Beyer, Maximilian; Grimm, Manuel; Lauster, Moritz; Müller, Dirk; Seifert, Joachim; Stergiaropoulos, Konstantinos

doi:10.1016/j.jobe.2019.101032

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…The comparison is thus aimed at evaluating the effectiveness of the standard methodology to properly account for the penalization effects due to the operation dynamics. On the other hand, quantitative comparisons with similar works, in which a modulating air-to-water heat pump is monitored when coupled to a HiL system [10] or to an existing building [29,30], or it is fully simulated under dynamic building-coupled conditions [31], are somehow misleading, since energy performances are very heterogeneous, being strongly influenced by climatic conditions and building and heat pump characteristics. However, in these cases, a meaningful parameter for comparison purposes is the penalization factor due to partial loads (see Appendix A).…”

Section: Resultsmentioning

confidence: 99%

“…Several scientific and technological issues in the field of HP systems are still unsolved. One of those is the problem of the efficiency reduction under dynamic operating conditions [10]. Especially in mild climates conditions and during intermediate seasons, the actual thermal load profile results are notably lower than the design capacity and intermittent operation occurs.…”

Section: Introductionmentioning

confidence: 99%

“…Other technical units may also be substituted by appropriate emulators, to be integrated with the energy system. Using real weather data, the tested devices (e.g., the heat pump) experience the same working conditions and load profiles that they would experience in a real application [9,10,13]. HiL experimentation is also characterized by a great flexibility, because it can test and optimize many different building-HVAC configurations and control strategies [17,20,21].…”

Section: Introductionmentioning

confidence: 99%

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Experimental Analysis of an Air Heat Pump for Heating Service Using a “Hardware-In-The-Loop” System

et al. 2020

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Estimating and optimizing the dynamic performance of a heat pump system coupled to a building is a paramount yet complex task, especially under intermittent conditions. This paper presents the “hardware-in-the-loop” experimental campaign of an air-source heat pump serving a typical dwelling in Pisa (Italy). The experimental apparatus uses real pieces of equipment, together with a thermal load emulator controlled by a full energy dynamic simulation of the considered building. Real weather data are continuously collected and used to run the simulation. The experimental campaign was performed from November 2019 to February 2020, measuring the system performances under real climate and load dynamics. With a water set point equal to 40 °C, the average heat pump coefficient of performance was about 3, while the overall building-plant performance was around 2. The deviation between the two performance indexes can be ascribed to the continuous on-off signals given by the zone thermostat due to the oversized capacity of the heat emission system. The overall performance raised to 2.5 thanks to a smoother operation obtained with reduced supply temperature (35 °C) and fan coil speed. The paper demonstrates the relevance of a dynamic analysis of the building-HVAC system and the potential of the “hardware-in-the-loop” approach in assessing actual part-load heat pump performances with respect to the standard stationary methodology.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental Analysis of an Air Heat Pump for Heating Service Using a “Hardware-In-The-Loop” System

et al. 2020

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“…The system consists of an air-to-water heat pump test bench in split design, which is fully controllable, test benches emulating the boundary conditions (climatic chamber and hydraulic test bench), simulation models for the building and the heat pump controller, and the cloud-based data infrastructure (MQTT-Broker and InfluxDB tick-stack). The used HiL approach and the corresponding test benches are well described in the literature [19], [20]. In this study, we use a self-developed heat pump test bench where we can freely control the compressor speed.…”

Section: Methodsmentioning

confidence: 99%

The Impact of Controller Settings in Heat Pumps: Numerical Findings and Experimental Verification

Göbel,

Kevin,

Christian

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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In residential buildings, the efficiency of heat pump systems (HPS) significantly depends on the HPS design and operation. In particular, HPS controllers often use ambient temperature-dependent heating curves for operation. The resulting supply temperature serves as the reference variable for the internal controller, which is used to manipulate the compressor speed. Typically, the internal controller has constant parameters for the PI controller, hysteresis, and operational time. While buildings have a highly dynamic, time-variant demand, these dynamics are rarely considered in controller development. This work investigates internal control parameters' sensitivity to the overall HPS efficiency in annual, dynamic simulations and experiments. To consider different supply temperatures, two buildings with underfloor heating and radiators serve as case studies. Based on a validated simulation model, the one-factor-at-a-time sensitivity analysis method determines the controller's influence on the seasonal coefficient of performance (SCOP). Hardware-in-the-loop experiments are conducted for representative periods extracted from annual simulation results for experimental verification. The results for the building with underfloor heating and radiators prove that the control parameter influences the SCOP up to 18.5 % and 4.2 %, respectively. In particular, different control parameters for the optimal operation were determined for both case studies, challenging the constant settings used in the state-of-theart. In addition, we observe a significant increase of 300 % in the avoidable compressor starts in experiments due to poor parameter settings. To ensure maximum efficiency of HPS and significantly reduce the number of compressor starts in any residential building, we recommend integrating adaptive setting of control parameters into the HPS controller.

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“…Simulation models should always be refined by experimental measurements; to this aim some emerging techniques are available today, such as the "hardware-in-the-loop" (HiL) experimentation methodology. The general elements of this approach can be found in [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Low-Capacity Heat Pump Systems for Residential Buildings through Thermal Energy Storage

et al. 2021

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The paper provides results from a hardware-in-the-loop experimental campaign on the operation of an air-source heat pump (HP) for heating a reference dwelling in Pisa, Italy. The system performances suffer from typical oversizing of heat emission devices and high water-supply temperature, resulting in HP inefficiencies, frequent on-off cycles, and relevant thermal losses on the hydronic loop. An experimentally validated HP model under different supply temperatures and part-load conditions is used to simulate the installation of a thermal storage between heat generator and emitters, in both series and parallel arrangements. Results relative to a typical residential apartment show that the presence of the thermal storage in series configuration ensures smoother heat pump operation and energy performance improvement. The number of daily on-off cycles can be reduced from 40 to 10, also saving one-third of electric energy with the same building loads. Preliminary guidelines are proposed for correctly sizing the tank in relation to the HP capacity and the average daily heating load of the building. A storage volume of about 70 L for each kilowatt of nominal heating capacity is suggested.

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Dynamic evaluations of heat pump and micro combined heat and power systems using the hardware-in-the-loop approach

Cited by 9 publications

References 16 publications

Experimental Analysis of an Air Heat Pump for Heating Service Using a “Hardware-In-The-Loop” System

Experimental Analysis of an Air Heat Pump for Heating Service Using a “Hardware-In-The-Loop” System

The Impact of Controller Settings in Heat Pumps: Numerical Findings and Experimental Verification

Optimal Operation of Low-Capacity Heat Pump Systems for Residential Buildings through Thermal Energy Storage

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