Jörg Worlitschek

The transition to renewable energy systems is constrained less by technological limitations than by social acceptance issues. Yet, existing energy system optimisation models largely neglect this dimension, representing social acceptance, if at all, as exogenous parameters or single-value thresholds. This study shows that modelling social acceptance as an endogenous function of technology and infrastructure characteristics alters optimal energy system design. A socio-techno-economic optimisation framework is presented that integrates empirically derived acceptance functions based on large-scale conjoint survey data, capturing multidimensional stakeholder preferences. The approach is demonstrated for the case of seasonal thermal energy storage, a technologically viable yet underadopted solution for decarbonising building energy supply. Applying the framework to two spatial scales, the single-family house and the district level, in a Swiss case study, the study demonstrates that integrating social acceptance shifts optimal solutions toward greater energy system resilience. The effect of social acceptance on optimisation results varies across technologies and scales, highlighting the multidimensional nature of technology preferences and underscoring the necessity of context-specific data and optimisation. The approach further supports a forward-looking perspective by enabling the evaluation of ``what if'' scenarios. This study carries significant implications for both research and practice. It proposes an effective interdisciplinary strategy that integrates the preferences of future implementers and users into the technology development process from an early stage, which may contribute to narrowing the gap between the existence of technological solutions and their actual implementation. In doing so, it also contributes to a reframing of what ``optimal'' means and, thus, to a more realistic and actionable modelling approach to effective energy system transition.

This review presents a technology roadmap for Thermal Energy Storage (TES) systems operating in the medium-temperature range of 100–300 °C, a critical window that accounts for approximately 37% of industrial process heat demand in Europe. Decarbonising this segment is essential to meeting climate targets, especially in sectors that are reliant on fossil-fuel-based steam. The study analyses 11 TES technologies, including sensible, latent, and thermochemical systems, covering both mature and emerging solutions. Each technology is evaluated based on technical, environmental, and socio-economic key performance indicators (KPIs), such as energy density (up to 200 kWh/m³), cost per storage capacity (€2–100/kWh), and technological readiness level (TRL). Sensible heat technologies are largely mature and commercially available, while latent heat systems—especially those using nitrate salts—offer promising energy density and cost trade-offs. Thermochemical storage, though less mature, shows potential in high-cycle applications and long-term flexibility. The review highlights practical configurations and integration strategies and identifies pathways for research and deployment. This work offers a comprehensive reference for stakeholders aiming to accelerate industrial decarbonisation through TES, particularly for applications such as drying, evaporation, and low-pressure steam generation.

Latent heat thermal energy storage (LHTES) based on phase change materials (PCMs) is receiving increasing interest since it offers high energy storage density while enabling the integration of variable renewable energies, hence boosting the transition towards a climate-neutral future. Despite the advantages that PCMs offer in providing a nearly isothermal solid–liquid phase transition, they still face some challenges that limit their deployment in real applications such as low thermal conductivity, phase separation, and supercooling, which affect charging and discharging rates. X-ray computed tomography (XCT) is a non-destructive imaging technique widely used in materials science for both qualitative and quantitative analysis of material microstructures and their evolution. Recent advances in laboratory-XCT instrumentation enabled short acquisition times on the order of tens of seconds which allows the investigation of dynamic processes in situ by time-lapse XCT measurements. These advances open new opportunities for revealing information on the morphology of solid–liquid PCMs. Despite the fact that XCT imaging has significant potential for energy research, its application in the field of PCMs is fairly new. A key enabler of applications of XCT to PCMs is the density difference between solid and liquid PCMs, which was found to be higher than 7% for all investigated PCMs. This enabled solid and liquid phases to be distinguished one from the other and properly quantified over time. The present work reviews the principles of laboratory-based XCT and the recent applications of XCT technology in the characterisation of PCMs, with emphasis on the study of the solid–liquid phase transition and validation of numerical PCM models by addressing the potentialities and challenges of XCT in PCM research.

Latent Heat Thermal Energy Storage (LHTES) based on phase change materials (PCM) is receiving increasing interest, since it offers high energy storage density while enabling the integration of variable renewable energies, hence boosting the transition towards a climate-neutral future. Despite the advantages that PCMs offer in providing a nearly isothermal solid–liquid phase transition, they still face some challenges that limit their deployment in real applications such as, low thermal conductivity, phase separation and supercooling, which affect charging and discharging rates. X-ray computed tomography (XCT) is a non-destructive imaging technique widely used in materials science for both qualitative and quantitative analysis of material microstructures and their evolution. Recent advances in laboratory-XCT instrumentation enabled short acquisition times on the order of tens of seconds which enable the investigation of dynamic processes in-situ by time-lapse XCT measurements. These advances open new opportunities for revealing information on the solid-liquid PCMs. Despite XCT imaging has significant potential for energy research, its application in the field of PCMs is fairly new. The present work reviews the principles of laboratory-based XCT and the recent applications of XCT technology in the characterisation of PCMs, with emphasis on the study of the solid-liquid phase transition and validation of numerical PCM models.

Nowadays, Latent Thermal Energy Storage systems (LTESs) are becoming the key enabling technology for the achievement of the 2050 EU targets on energy savings and CO₂ emission reduction. LTESs are based on Phase Change Materials (PCMs), which, during the liquid-solid phase change, can store and release large amount of thermal energy compared to sensible systems. Several studies have already been conducted to properly design LTESs based on PCMs and many of those aimed at developing reliable numerical tools. The main approach to model the phase change with a Computational Fluid Dynamics (CFD) approach is the enthalpy-porosity method, which has already been successfully validated in several articles, especially against the average PCM temperature. However, due to the experimental complexity, the liquid fraction evolution is hard to be measured and monitored and only symmetrical systems could have been tested so far. X-ray Computed Tomography (CT) is a strong and reliable tool to experimentally evaluate the liquid fraction profile during either the melting or solidification process in real geometries and is extremely useful to validate the numerical models. In the present study, a first attempt to combine the described experimental-numerical approach is proposed: an enthalpy-porosity model was developed and validated against liquid fraction data from a computed tomography system. The liquid fraction profile of an ice cylinder melting at ambient temperature was measured using the CT and then compared against the numerical results. The results show the promising capabilities of the proposed methodology.