Stochastic optimization of thermal energy storage for multi-energy systems with hydrogen and renewable integration
Lalji Kumar, Uttam Kumar Khedlekar
Abstract
This study proposes a novel, unified techno-economic and optimization framework that integrates thermal energy storage (TES) into multi-energy systems, explicitly highlighting its critical role in balancing renewable intermittency and enhancing system flexibility. Addressing the increasing demand for scalable, cost-effective, and flexible storage technologies, this research uniquely combines sensible, latent, thermochemical, and hybrid TES methods with battery storage and hydrogen production, optimized via advanced stochastic modeling techniques. Employing real-world weather data and multi-day load scenarios, the developed stochastic optimization model demonstrates significant technical and economic benefits across detailed case studies in district heating (Spain), hydrogen production (Germany), and seasonal borehole thermal energy storage (Canada). Results clearly underscore the framework’s effectiveness, achieving remarkable emission reductions of up to 65% and cost savings exceeding 30% compared to conventional scenarios without TES integration. Key performance indicators, including levelized cost of storage, round-trip efficiency, carbon offset potential, and net present value, further emphasize TES’s superior performance and economic feasibility in renewable-rich environments. This research provides robust evidence supporting TES integration as essential for carbon-neutral, resilient, and economically dispatchable renewable energy infrastructures, offering a replicable modeling approach and valuable insights for researchers, policymakers, and energy system planners.