Techno-economic optimization of an energy system with sorption thermal energy storage in different energy markets

Luca Scapino (Corresponding author), Carlo De Servi, Herbert A. Zondag, Jan Diriken, Camilo C.M. Rindt, Adriano Sciacovelli

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16 Citations (Scopus)
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Sorption thermal energy storage (STES) has the potential to have higher energy densities and lower thermal losses compared to conventional thermal storage technologies, and it can contribute to increase the energy grid flexibility and the penetration of intermittent and distributed energy sources. However, STES is a technology still under research, and system-scale investigations are necessary to determine its potential in future energy systems. In this regard, the objective of this work is to investigate the STES potential in a reference energy system interacting with different energy markets. The system consists of a geothermal doublet supplying thermal energy to an organic Rankine cycle (ORC) and to a district heating network that satisfies the thermal energy demand of a residential neighborhood. A techno-economic optimization of the energy system is carried out using mixed integer linear programming. The optimization aims at finding the optimal STES size and system operational behavior that maximizes the yearly profits from selling the ORC energy to the energy markets. Among the main results, it is found that the STES integration increased the overall system profits by 41% in the scenario where the ORC interacted with the UK day ahead market (2017 data), and with two UK balancing services: the capacity market, and the short term operating reserve. In conclusion, this work highlights how a thermal storage technology still under research could become an asset under specific market conditions. Future policy mechanisms can benefit from similar analyses and foster the integration of new technologies into the energy grid.
Original languageEnglish
Article number114063
Number of pages19
JournalApplied Energy
Publication statusPublished - 15 Jan 2020


  • Sorption thermal energy storage
  • Energy systems optimization
  • Energy markets
  • Mixed integer linear programming
  • Organic Rankine cycle


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