Demonstration of sector-coupling based on advanced Thermal Energy Storage: a Model Predictive Control framework for load-shifting and grid-balancing

The roadmap for urban sustainability involves the transition to reliable and decarbonised energy networks. In this regard, business concepts based on sector coupling through the use of Thermal Energy Storage (TES) systems can play a key role. This research is placed in this context, with the aim of evaluating the flexibility potential of novel TES in order to provide load shifting services to the electricity grid and improve the renewables penetration. The idea involves the modelling of the TES upscaling scenarios on the national territory and the simulation of energy demand starting from real data on the electricity grid from European TSOs. For this purpose, a Model Predictive Control Framework (MPC) is developed and implemented in Python environment and the results for the case study of Italy are presented. Starting from the time-series data of energy production and consumption at national level, the actual fraction of electricity used for heating and cooling is calculated and the potential of using short-term and mid-term thermal energy storage for minimizing the surplus from renewable energy sources (RES) in the grid is evaluated. As a result, alternative hourly load profiles based on load shifting are proposed and the flexibility potential and sustainability impact of such systems is discussed. The findings show a reduction of 57 % per year of the RES surplus with values close to 100 % during winter days under the considered thermal energy storage capacity scenario. In addition, at least 10 % load shifting potential is achieved. The research provides a contribution to the demonstration and optimization of sector coupling concepts and discusses future outlooks and directions. Lessons learned can constitute insights for policy makers and technology providers, boosting research and diffusion of thermal energy storage technologies as an alternative to batteries and hydrogen systems for unlocking the flexibility potential of electric grids. • The energy flexibility potential of novel Thermal Energy Storage (TES) is analyzed. • PCM and TCM upscaling scenarios on the national territory are modelled in Python. • Real data on the electricity grid and laboratory data on TES are used as an input. • A Model Predictive Control (MPC) Framework is developed for the case study of Italy. • Significant load-shifting and a 57 % yearly reduction of the RES surplus are achieved.