Stochastic optimization of thermal energy storage for multi-energy systems with hydrogen and renewable integration
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".