Socio-techno-economic optimization of energy systems with seasonal thermal storage: a study on a Swiss single-family house

Optimal design and operation of buildings’ energy systems with seasonal thermal energy storage (STES) is vital to ensure their efficacy. However, people’s acceptance of the storage device is also crucial in determining the best technology choice. In this work, we modelled social acceptance of STES with logistic regression based on data from conjoint analysis among citizens, with seven features related to the technical and economical characteristics of storage.

Integrating this model into a socio-techno-economic optimization framework, we found the best sizing and operation of the storage as these three aspects are differently prioritized. The proposed mixed-integer quadratically-constrained programming formulation was applied to a Swiss single-family house case study, with a PV array, heat pumps, water- or phase change material (PCM)-based storage.

Results showed that minimizing costs leads to a small optimal storage size while minimizing CO2 emissions results in the largest considered size. The optimal size when maximizing acceptance is larger than that of minimizing costs, achieving a significant further reduction of CO2 emissions and improving the thermal self-sufficiency of the house. Additionally, water-based storage is always more accepted than PCM-based storage.