A probabilistic approach for assessing the potential capacity of open-loop Ground Source Heating and Cooling (GSHC) and Aquifer Thermal Energy Storage (ATES) deployments implemented in open-source software

Ground Source Heating and Cooling (GSHC) and Aquifer Thermal Energy Storage (ATES) systems offer low carbon heating and cooling.  Selecting the best technology or combination of technologies for a given installation requires estimates of system performance early in the design process when detailed, site-specific data are not available.  It is common for early performance estimates to test only a few selected values of key input parameters.  This approach fails to capture the range of potential performance, or the probability of a given performance, and does not allow identification of key uncertain parameters that impact predicted behaviour. 

He we present a simple methodology for rapid, probabilistic assessments of GSHC and ATES system performance from uncertain data using a Monte-Carlo approach.  The method does not require complex numerical simulations; rather, it allows the use of data from analogue systems to guide the range of input parameters that impact performance.  It is assumed that the system under analysis is energy balanced and approximately volume balanced, and that system parameters and predictions can be represented using average values over a given heating or cooling cycle.  The methodology is implemented in an open-source software tool.  The method and software are demonstrated using a case study of Imperial’s South Kensington campus in London.