Dealing with uncertainty in global warming impact assessments of refrigeration systems

Abstract: The United Nations recognises anthropocentric greenhouse gas emissions to be the leading cause of global warming. The International Institute of Refrigeration further addresses that in 2014 7.8% of the global greenhouse gas emissions were assigned to the refrigeration sector. This marked the importance of using metrics to evaluate the climate impact of a refrigeration system. However, as these metrics rely on uncertain values it is difficult to assess how reliable they are. The purpose of this study is therefore to evaluate the reliability of two environmental metrics by applying methods for dealing with uncertainties, and to present possible improvements to the applied methods and metric. The study begins by introducing refrigeration systems and their environmentalcontext. In the background the reader is further introduced to the topic by accounting for the evaluated metrics, TEWI and LCCP, as well as three different methods for dealing with uncertainties, Sensitivity analysis, Uncertainty analysis and Monte Carlo Simulation. In order to fulfil the purpose a data centre is modelled, and the restrictions and operation conditions of the system will be further described under section 3. The result will consist of two parts. The first part will consider the theoretical aspect of the study as well as sources and typologies of values and uncertainties. The second part will consist of the empirical results from applying the mentioned methods on the modelled system. These will be presented in graphs sorted after method and metric and are then analysed and evaluated in the discussion. It is seen that only a few parameters dominate the influence in the Sensitivity and Uncertainty analysis but that the influential parameter is dependent on the relative order of magnitude. It is also stated that the LCCP rends no additional information at the analysed conditions. When applying the Monte Carlo Simulation TEWI is considered more reliable, as in that the deterministic value is a more accurate estimation of the ’true’ environmental impact of the system. One possible improvement may be to use the rendered standard deviation for TEWI as an uncertainty range to incorporate the uncertainties in the deterministic value. The study concludes that the Sensitivity and Uncertainty analysis illustrates theinfluence of one single parameter on the final metric value. However, the analyses do not determine to what extent these final values may be considered reliable. A Monte Carlo Simulation is better applicable for some uncertainty typology than others and as such TEWI is considered more reliable than LCCP. The study lands in the conclusion that the presented methods may be improved by assigning uncertainty typologies in order to evaluate the viability of a method to incorporate the uncertainties, e.g. a Monte Carlo Simulation.