Europe’s transition to a climate-neutral energy system requires new ways to store and deliver renewable energy on demand. Many industrial sectors and power applications operate at very high temperatures (>1000oC) and cannot rely solely on electricity from intermittent sources, such as solar and wind. Conventional batteries are unsuitable for these conditions, while existing thermal storage systems often lack efficiency and flexibility.
SHINE addressed this challenge by exploring latent-heat thermophotovoltaic (LHTPV) batteries, an emerging ultra-high-temperature energy storage technology. These systems store thermal heat in metallic-based phase-change materials and convert it directly into electricity using solid-state converters, i.e. thermophotovoltaic (TPV) converters in this case. They have the potential to support dispatchable renewable power, improve energy efficiency and contribute to the decarbonisation of energy-intensive industries, aligning strongly with the goals of the European Green Deal. Their applications can be various from waste heat recovery, to power-to-heat-to-power and solar-to-heat-to-power concepts, with the last two being investigated within SHINE.
Before such systems can be deployed, however, the scientific community needs accurate yet computationally efficient modelling tools capable of predicting their behaviour under real operating conditions, as well as accessible datasets that support transparency and further innovation. SHINE was therefore designed to fill a critical knowledge gap by developing advanced multi-scale numerical models for the battery two key components (storage system and converter), public datasets and a computationally efficient reduced-order model capable of simulating the full LHTPV battery. The produced-order model has retrieved information from full order models leading to next generation simulation tools.
SHINE overall objectives were:
1. To build and validate through in-house experiments full order models (FOMs) of the system’s two key components: the latent-heat thermal storage unit (thermal model) and the TPV converter (electric model);
2. To generate and publish numerical datasets using the FOMs;
3. To generate Artificial Neural Networks (ANNs)-based multivariable polynomial functions predicting the sub-components performance, using data from the produced datasets;
4. To integrate the models into a fast, simplified in-house simulation platform suitable for optimisation, system análisis, measured data post-processing and clean-up and future integration with experimental measurements;
5. To study the dynamic behaviour of the LHTPV battery under different charging and discharging conditions.
The expected impacts include:
• Improved scientific understanding of ultra-high-temperature energy storage and conversion;
• A new modelling paradigm of using AI to feed ROMs without losing information from rigorous models;
• The importance of deploying multi-scale, multi-disciplinary research for next-generation energy storage and covnersion technologies;
• Generation of new tools for supporting system design, optimisation and monitoring;
• Data sets and in-house codes that enable future digital twins and improved control strategies (posible integration into open and closed-loop systems);
• Long-term benefits for renewable energy integration in industrial and power sectors.
While SHINE is a technical project, it also contributes to broader societal needs by supporting the transition toward sustainable and secure energy systems. Through open data, communication actions and training activities, the project has encouraged knowledge sharing across both industrial and academic stakeholders.
