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The ground source heat pumps (GSHPs) of customers connected to the district heating and cooling (DHC) network can benefit both the customer and the energy company. However, operating the GSHP to minimize the cost of providing heating and cooling to the customer while ensuring the long-term stability of the ground temperature is a challenge. This thesis addresses the challenge by developing accurate models of GSHP and optimizing the operation of the GSHP system using these models. The models presented in this thesis use field measurements to develop accurate models with low computational time. The main components of a GSHP system are the heat pump and the borehole heat exchanger (BHE). This thesis presents two approaches to use measured data to improve the accuracy of analytical models for BHE. The first approach is the calibration of the model parameters using this measured data. The second approach combines the analytical model with an artificial neural network model resulting in a hybrid model. The calibration approach reduced the relative RMSE of the analytical model from 21.9% to 13.9% in the testing period. The relative RMSE of the hybrid model for the testing period was 6.3%. We compared different data-driven models for heat pumps and determined that artificial neural network models have an advantage over traditional regression models when field measurements are available. The artificial neural network model was refined to better utilize the measured data. The refined models of heat pumps had a relative RMSE of less than 5%. The hybrid BHE model and an artificial neural network model for the heat pumps were used to model the GSHP system. The model was validated using four years of field measurements. The relative MAE for the compressor power and BHE power were 7.3% and 19.1% respectively. The validated model was used to optimize the operation of the GSHP system. In optimal operation, the cost of providing heating and cooling to the area was minimized from the perspective of the energy company while maintaining a stable temperature in the ground. In optimal operation, the annual cost of operation was shown to reduce by 64 t€ and the annual CO2 emission was shown to reduce by 92 tons. Bergvärmepumpar som är anslutna till fjärrvärme- och kylnät kan vara till fördel både för användare och leverantörer av energi. I detta sammanhang utgör emellertid driftstrategin en stor utmaning för att minimera energikostnaden för att tillhandahålla värme och kyla och samtidigt säkerställa att marktemperaturen långsiktigt blir stabil. En viktig målsättning med denna avhandling har därför varit att förfina och utveckla modeller för driftoptimering av ett bergvärmepumpsystemi samverkan med ett fjärrvärmenät. Huvudkomponenterna i ett bergvärmepumpsystem är värmepumpen och borrhålsvärmeväxlaren. Denna avhandling presenterar två metoder för att använda verkliga driftdata med syftet att förbättra noggrannheten hos analytiska modeller för borrhålsvärmeväxlaren. Det första tillvägagångssättet är kalibrering av modellparametrarna med hjälp av uppmätta data. Det andra tillvägagångssättet kombinerar den analytiska modellen med en artificiell neural nätverksmodell som resulterar i en hybridmodell. Kalibreringsmetoden reducerade den analytiska modellens standardavvikelse från 21,9% till 13,9% under testperioden. Standardavvikelsen för hybridmodellen för testperioden var 6,3%. Vid jämförelsen av olika datadrivna modeller för värmepumpar konstaterades det att artificiella neurala nätverksmodeller har en fördel jämfört med traditionella regressionsmodeller då fältmätningar är tillgängliga. Den artificiella neurala nätverksmodellen förfinades för att på bästa sätt nyttja uppmätta data. Med de förfinade modellerna erhölls en standardavvikelse på mindre än 5%. Borrhålsvärmeväxlarens modell och en artificiell neural modell för värmepumparna användes för att modellera bergvärmepumpsystemet. Modellen validerades med driftdata från fyra års fältmätningar. Det relativa medelfelet för kompressoreffekten och borrhålsvärmeväxlarens effekt var 7,8% respektive 19,1%. Den validerade modellen användes för att optimera driften av bergvärmepumpsystemet. Vid optimal drift minimerades kostnaden för att tillhandahålla uppvärmning och kyla sett ur energileverantörens perspektiv, samtidigt som en stabil temperatur i marken bibehölls. Vid optimal drift visade sig den årliga driftskostnaden minska med 64 t€ och det årliga koldioxidutsläppet visade sig minska med 92 ton.

Monitoring a ground source heat pump can provide important insights into its working, but to study the behaviour of the borehole heat exchanger (BHE) we require monitored data for the whole period of operation. In practice, the monitored data often has periods of missing data. We propose a method to estimate the load during the periods of missing data based on the fluid temperature after that period. The method determined the missing load with negligible error, for the case of a BHE that behaves exactly like the model describing it. A sensitivity analysis showed that the estimated load is highly sensitive to errors in measured load and fluid temperature. The method was applied to a real monitored BHE, the magnitude of estimated loads were unreasonably high, but the overall deviation between the measured and simulated values of fluid temperature decreased. Therefore, the high magnitude of missing load compensates for the lack of agreement between the model and the measured data.

In the mathematical simulation of bore fields for ground coupled heat pump (GCHP) systems, it has become a common practice to consider the boreholes as having a uniform temperature. As a rule the boreholes are hydraulically connected in parallel and the small temperature difference between incoming and outgoing heat carrier fluid justifies the assumption that all boreholes have the same uniform temperature in operation. Two simultaneous boundary conditions usually apply: All borehole walls should have a uniform temperature and the heat flow from the bore field should equal the energy needed by the heat pump. This paper describes improvements applied to a previous numerical approach that employs the concept of a highly conductive material (HCM) embedded in the boreholes and connected to a HCM bar above the ground surface to impose a uniform temperature boundary condition at the borehole wall. The original boundary condition with the uniform fluid temperature comes in conflict with the concept of the uniform borehole wall temperature. Between the fluid and the borehole wall there is a thermal borehole resistance. The heat flux increases at the borehole ends and thus also the temperature changes between borehole wall and the fluid. The borehole wall temperature deviates from the uniform assumption and will cause an error in the simulations. This paper presents a correction to that error. Firstly, the improvements to the HCM model are validated for g-function generation, which presents a good agreement with reference solutions. Secondly, the improvements to the HCM model are illustrated to predict fluid temperatures for measured variable daily loads of a monitored GCHP installation. The predicted fluid temperatures are compared with monitored data for about four years. The predicted fluid temperatures deviate from the measured data by less than 1 K during the last monitored year. QC 20160928