The main target of this paper is to numerically study a multi-source (air/sun/ground) heat pump with the implementation of a thermal energy storage, using either water or PCM, for residential space heating. The system was modelled considering several sub-models for each of the components (compressor, solar panels, storage tank, heat exchangers etc.). A control strategy has been established to decide which operating mode of the system provides the highest coefficient of performance (COP). A multi-objective optimization through genetic algorithm of several decisional variables of the system was carried out, in different configurations and climate conditions, by considering different scenarios in terms of total investment and energy consumption costs, to optimize seasonal performances and investment cost of the entire system. Results show that solar thermal and solar photovoltaic collectors coupled with water storage tank give higher seasonal energy performance, especially in warmer climates, whereas the exploitation of the ground source can be more advantageous for colder climates. From the optimization analysis, it results that optimal non-dominated solutions characterized by a SCOP increase between 50% and 250% are characterized by higher investment costs between 215% and 730%, depending on the climate conditions. None of the solutions employing a PCM storage tank results economically feasible, due to a slight effect on system performance, and a much higher effect on investment costs. Finally, several cost scenarios in terms of incentives on investment costs and increased energy prices were analysed, for which the employment of scenarios with higher capital investment can be more advantageous in terms of lower total costs.

This work was partially funded by Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (PID2021-123511OB-C31 - MCIN/AEI/10.13039/501100011033/FEDER, UE and RED2018-102431-T - MCIU/AEI). This work is partially supported by ICREA under the ICREA Academia programme. The authors form University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia.