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.

Demand side management (DSM) is a strategy for district heating (DH) networks to reduce peak demand and energy costs. Traditional DSM methods apply fixed temperature reductions, assuming uniform occupant tolerance, which can limit effectiveness or cause discomfort. This paper presents findings from a longitudinal field study (2023–2024 heating season, Stockholm, Sweden) evaluating a personalized DSM approach. Using the ComfortID mobile application, approximately 70 users could accept, or abort DSM events based on individual thermal comfort preference. The results showed that about 25% of events were cancelled. Accepted events averaged a 0.8-K reduction over 22 hours and 54 minutes; cancelled events showed a 1.1-°C reduction over 76 minutes. Additionally, the study found that participants’ thermal sensations significantly deviated from the ISO 7730 standard, highlighting the limitations of generic models. Incorporating personalized thermal models doubled occupant flexibility for DSM compared to a population-based approach. The results demonstrate that integrating personalization into DSM programs can enhance flexibility and energy savings up to 28% without compromising occupant's comfort.

The increasing integration of renewable energy sources (RES) and the transition towards a decarbonized energy sector present significant challenges, particularly in demand-side management. Thermal energy storage (TES) systems offer a cost-effective solution for enhancing energy flexibility in building heating systems. However, improper sizing and operation of TES systems can lead to increased investment costs and energy losses. To bridge this gap, this study proposes a novel, optimization-based framework for the systematic sizing and operation of TES systems. The methodology encompasses two key components: (1) an innovative TES sizing framework that integrates system modelling and optimization-based sizing leveraging historical thermal load data; (2) validation and performance evaluation of the sizing outputs through building energy simulations across three diverse building types and climatic conditions. Key findings demonstrate the framework's ability to adapt to various scenarios, achieving operational cost reductions of up to 35 % and significantly enhancing the energy flexibility in terms of flexibility factor by up to 1.03. Furthermore, the proposed framework is shown to effectively optimize TES capacities to unique building load patterns. These results highlight the framework's potential as a robust tool for optimizing TES in buildings, contributing to flexible and cost-efficient energy systems.