Building on the results of the first funding period, the second phase will enhance the agent-based simulation model to investigate additional applications and use cases for the Carnot Battery (CB) while keeping the philosophy of inverse modelling. The already developed methodological frameworks will be further refined and extended to support the investigations of individual perspectives and incentives to invest in as well as to operate CBs. A key aspect of the project is the potential to capture additional value through capacity remunerations in future energy markets, as results from the first phase indicate that short-term market revenues may be limited. This additional potential is of high interest because future energy markets are expected to exhibit increased variability and complexity due to the ongoing expansion of renewable generation. The promising economic potential offered by Capacity Renumeration Mechanisms (CRMs) is therefore essential for guiding both the design and deployment strategies of the technology. The inverse methodology must be adapted to this framework. Furthermore, the project seeks to enhance the technical representation of CBs. A more detailed modelling framework tailored to behind-the-meter use cases enables comprehensive depiction of the battery’s operational potential and allows for exploration of multi-sector applications. This not only improves the technical fidelity of the model but also provides insights into the battery’s operational behaviour and a clearer understanding of its technical constraints. Finally, the project advances the framework developed in the first funding period for assessing risk structures from an investor’s perspective. Whereas the initial phase focused primarily on mean-reverting and path-dependent uncertainties, the new project will extend this framework to material supply risks and their potential implications for investor decision-making. Accounting for a broad spectrum of uncertainties is critical not only for the identification of relevant use cases but also for understanding the economic viability and risk profiles of CB applications. This risk assessment is expected to provide robust insights that can guide investment, deployment, and policy decisions under uncertain market conditions. In summary, the objectives described broaden the initial analysis. The integration of CRMs provides insights into agents’ economic incentives and how these opportunities shape investment decisions. The improved technical representation and the extended incorporation of risks complement the analysis. Therefore, the results provide innovative guidelines for the technical research areas of the Priority Programme, which is fully consistent with the inverse top-down approach.

Professor Dr. Wolf Fichtner
Karlsruher Institut für Technologie
Institut für Industriebetriebslehre und Industrielle Produktion
Lehrstuhl für Energiewirtschaft

Energy storage is crucial for transitioning to sustainable energy systems. Many existing technologies, however, face challenges in terms of limited expansion potentials, requirements of critical materials, or the dependency on specific geological formations. Carnot Batteries (CBs) offer a promising alternative, yet their market competitiveness and role in future energy systems remain uncertain due to ongoing technological development and evolving market conditions. This research aims to bridge the gap between energy system modelling (ESM) and technology development, specifically for CBs. Recent works within the priority programme 2403 have made progress in this area. A multi-objective inverse (MOIn) methodology for ESM has been developed within research area A of the priority programme to analyse the requirements and opportunities of CBs from the perspective of energy systems and markets. MOIn turns highly uncertain quantities of technologies under development, such as cost or efficiency, which are typically input parameters for ESMs, into optimisation variables. The application of the MOIn methodology to the Central Western European (CWE) market area shows that CBs face challenges in achieving economic competitiveness in wholesale power markets, which is confirmed by further studies from research area A. Altogether, recent studies, including our own, suggests future research should focus on considering additional drivers for CB deployment, such as material supply risk. Moreover, future work should study behind-the-meter (BTM) applications of CBs and heat integration. The objective of this project is therefore to further improve and develop the inverse method initiated in the first funding period. On the one hand, we will extend the MOIn method to another system-level objective (material supply risk). On the other hand, we will develop a novel method for inverse modelling to generate alternatives (MGA) in order to invert multiple technology parameters into optimisation variables. This will allow to explore the near-optimal solution space for alternatives with different, yet still competitive, techno-economic parameter configurations. These new methods will be demonstrated to generate new insights on development requirements and market potentials for CBs. The implications of considering material supply risk will be studied for large-scale energy systems, reassessing previous findings. The inverse MGA method will be applied to a small-scale BTM system with coupled power and (<40°C) heat supply. Finally, this project seeks to deepen existing and establish new ways of collaboration between energy system modellers and technology development research within and beyond the priority programme’s research areas B and C.

Professor Dr. Valentin Bertsch
Ruhr-Universität Bochum
Fakultät für Maschinenbau
Lehrstuhl für Energiesysteme und Energiewirtschaft