Improved Methods for Design and Sizing of the Heating Components for Passive Houses
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The aim of this report is to investigate the potential for improvements of the design and sizing of heating systems in passive houses. This is carried out by analyzing the heat demand in a building called "The Climate and Energy Laboratory", and utilizing simulation data for an improved sizing of the components of the heating system. Over-sizing heating systems has been proven to be a challenge in passive houses, and there are often large deviations between the measured and the calculated heat load. This leads to extra costs and increased energy use. The lack of a described method for calculation of the heat load is one of the main reasons for the incorrect sizing. The Climate and Energy Laboratory, with a total floor area of 137.3 m$^2$ is localized in Otta in Norway, and is designed as a learning platform with the aim to promote the focus on renewable and sustainable solutions in buildings. The building is connected to Nord-Gudbrandsdal Upper Secondary School, and produce heat using an air-to-water heat pump, solar collectors and an electric boiler. Electricity is produced on-site from PV panels and a wind turbine. This building is used as reference building, and the implementation of two heating systems is investigated. The components in these scenarios are optimized with respect to two objective functions; minimizing delivered energy for heating and minimizing the life cycle costs. Scenario 1 includes heat production by the use of an air-to-water heat pump and an electric boiler for peak loads, whereas scenario 2 uses external district heating. In both scenarios, radiators and heating battery in the ventilation system are applied for space heating. The domestic hot tap water demand at the Climate and Energy Laboratory is minimal, and is consequently neglected. In order to achieve systems with the lowest possible supplied energy for space heating and systems with minimized LCC, the main design- and operational parameters must be optimized. To find the optimal values, the dynamic simulation tool IDA Climate and Energy 4.7 was applied. In the parametric analysis, one parameter was changed at the time, and the model was further applied in the optimization of the next parameter. Constraints securing a sufficiently good indoor environment was also included. The model in IDA ICE was established based on collected data regarding the building, and thereafter calibrated with respect to measured heat demand on hourly basis. There are several uncertain factors affecting the results throughout the report, and an uncertainty analysis was performed, including a sensitivity analysis of the most uncertain factors. The design heat load for the building was calculated to 22.9 kW, using the method described in standard NS-EN 12831. For the initial, calibrated model, the maximum heat load was calculated to 18.1 kW based on DOT, and 10.7 kW based on the analyzed year. This correspond to reductions of 6.3 \% and 53 \% compared to the initial model. After the parametric analysis, where the components were optimized with respect to both minimization of energy use and annual costs, the maximum heat loads were further reduced. For the scenario with the heat pump and electric boiler, the optimal heat load was 4.4 kW by minimization of the annual costs. In this case, the capacity of the heat pump and the electric boiler were 2 kW and 4 kW, respectively. By minimizing the energy use, the resulting heat load was 3.1 kW, and the heat pump did in this case cover the whole heat load with a capacity of 8 kW. For the second scenario, the maximum heat load was 4.7 kW. Compared to the calculated design heat load, the implementation of the operational measures resulted in a reduction in the total maximum heat load of 81 \%, 87 \% and 79 \%, respectively. The energy use and annual costs for the initial model was 5 902 kWh and 30 524 NOK. After the optimization of the components with respect to the two objective functions for scenario 1, the annual costs were reduced to 15 863 NOK and the energy use was reduced to 1 596 kWh. For scenario 2, the resulting annual costs and energy use were 12 983 NOK and 5 922 kWh. Consequently, scenario 1 is more beneficial with respect to energy use, whereas scenario 2 is preferred with respect to the annual costs. From the results of the analysis, it can be concluded that the current method may lead oversized components, causing higher investment costs, larger components requiring more space, and less efficient operation because of a higher share of operation on part load. Consequently, it is proposed that the calculation method for the design heat load should change from a static approach to a dynamic approach, as many of the factors influencing the heat load are dynamic and difficult to assume in a static model. Using the current method, the heating systems are sized for a fully heated building, having full operation of technical installations, having a hundred percent simultaneous use of the installations, but without including internal heat loads. Using a dynamic approach, including internal and solar gains, heat storage, simultaneity factor, control and operation, a more realistic DOT and a reduced safety factor could provide a more accurate calculation method for the design heat load. However, there are many uncertainties related to the study, and it is necessary to investigate several cases in order to propose a new, general, concrete calculation method for the design heat load.