Reinforcement learning-based exergetic analysis and assessment of a novel cogeneration system for smart urban energy applications
Abstract
This paper presents a novel Kalina-based geothermal cogeneration system for smart urban energy applications, integrated with a reinforcement-learning-based exergetic analysis and control framework implemented in a digital twin environment coupling EES, TRNSYS and COMSOL. The configuration exploits absorber-integrated internal heat regeneration to raise the working-fluid temperature above the geothermal source without auxiliary energy input. The optimisation results indicated that the reinforcement learning (RL)-based control strategy improved overall energy and exergy efficiencies by 0.066 and 0.058, respectively while simultaneously reducing CO2 emissions by 0.126, levelised cost of electricity by 0.191, and the levelised cost of heating by 0.133.
Fields of Study & Subjects
Thermodynamic and Exergetic Analyses of Power and Cooling Systems
Smart Grid Energy Management
Integrated Energy Systems Optimization
Publication details
- Journal / source
- International Journal of Exergy
- Publisher
- Inderscience Publishers
- Volume
- 49
- Issue
- 2
- Pages
- 122–142
- Publication date
- 1 January 2026
- Language
- EN