Provningsmetod för integrerade sol-biosystem : Årsverkningsgrad genom korttidsmätning

Test method for integrated solar- biomass systems - Long term prediction trough short term measurementsSP Technical Research Institute of Sweden and SERC, Dalarna University have in cooperation developed a test method for integrated solar and biomass systems. The test method is performed under six days including two summer days, two winter days and two spring/autumn days true to real weather conditions and loads for a single family house. The aim of the test method is to get information about a Combisystem’s annual performance and operation throughout a short term test. Seven different solar Combisystems have been tested within the project together with a pellet boiler without solar collectors. In addition to that a comparative testing has been performed at the two laboratories at SP and at SERC on the same Combisystem. The test method developed within the project has been proved to withstand the aim of the project, which is to be able to compare the performance between the systems. The test method is also suitable for identification of possible operation problems so they can be taken care of and consequently improves the system.The project and the system testing reveal that it is in general favorable to combine biomass pellets with solar heating. Pellet boilers has normally a low performance during the summer period but combined with a solar collector the boiler can be switch off during this period. There are however big differences in performance between the tested. The efficiency of the pellet boiler is highly dependent of the operating conditions and elements like heat losses from the system, system configuration and control strategy have great influence of the performance of the system and the emissions. On the other hand, the performance and the size of the solar collectors have a minor effect on the overall system performance. There is obviously a big potential for improvement of the system´s performance and the developed test method is an essential way to implement this perfection.

Characterization of Chimney Flue Gas Flows : Flow Rate Measurements with Averaging Pitot Probes

Performance testing methods of boilers in transient operating conditions (start, stop and combustion power modulation sequences) need the combustion rate quantified to allow for the emissions to be quantified. One way of quantifying the combustion rate of a boiler during transient operating conditions is by measuring the flue gas flow rate. The flow conditions in chimneys of single family house boilers pose a challenge however, mainly because of the low flow velocity. The main objectives of the work were to characterize the flow conditions in residential chimneys, to evaluate the use of the Pitot-static method and the averaging Pitot method, and to develop and test a calibration method for averaging Pitot probes for low

Dynamic whole system testing of combined renewable heating systems : the current state of the art

Objective: For the evaluation of the energetic performance of combined renewable heating systems that supply space heat and domestic hot water for single family houses, dynamic behaviour, component interactions, and control of the system play a crucial role and should be included in test methods. Methods: New dynamic whole system test methods were developed based on “hardware in the loop” concepts. Three similar approaches are described and their differences are discussed. The methods were applied for testing solar thermal systems in combination with fossil fuel boilers (heating oil and natural gas), biomass boilers, and/or heat pumps. Results: All three methods were able to show the performance of combined heating systems under transient operating conditions. The methods often detected unexpected behaviour of the tested system that cannot be detected based on steady state performance tests that are usually applied to single components. Conclusion: Further work will be needed to harmonize the different test methods in order to reach comparable results between the different laboratories. Practice implications: A harmonized approach for whole system tests may lead to new test standards and improve the accuracy of performance prediction as well as reduce the need for field tests.

ClimateWell TDC with District Heat

The PolySMART demonstration system SP1b has been modeled in TRNSYS and calibrated against monitored data. The system is an example of distributed cooling with centralized CHP, where the driving heat is delivered via the district heating network. The system pre-cools the cooling water for the head office of Borlänge municipality, for which the main cooling is supplied by a 200 kW compression chiller. The SP1b system thus provides pre-cooling. It consists of ClimateWell TDC with nominal capacity of 10 kW together with a dry cooler for recooling and heat exchangers in the cooling and driving circuits. The cooling system is only operated from 06:00 to 17:00 during working days, and the cooling season is generally from mid May to mid September. The nominal operating conditions of the main chiller are 12/15°C. The main aims of this simulation study were to: reduce the electricity consumption, and if possible to improve the thermal COP and capacity at the same time; and to study how the system would perform with different boundary conditions such as climate and load. The calibration of the system model was made in three stages: estimation of parameters based on manufacturer data and dimensions of the system; calibration of each circuit (pipes and heat exchangers) separately using steady state point; and finally calibration of the complete model in terms of thermal and electrical energy as well as running times, for a five day time series of data with one minute average data values. All the performance figures were with 3% of the measured values apart from the running time for the driving circuit that was 4% different. However, the performance figures for this base case system for the complete cooling season of mid-May to midSeptember were significantly better than those for the monitoring data. This was attributed to long periods when the monitored system was not in operation and due to a control parameter that hindered cold delivery at certain times.