Design and Operation Optimization of an Integrated Solar-Powered Organic Rankine Cycle System With Energy Storage
Bibliographic record
Abstract
Abstract Addressing the environmental and economic concerns related to energy use in buildings has prompted a global emphasis on energy sustainability by reducing dependence on fossil fuels and promoting cleaner alternatives. One such alternative for utilizing renewable sources is the organic Rankine cycle (ORC) system. In this paper, an integrated solar-powered organic Rankine cycle with battery energy storage (ORC-BES) system is proposed, and its performance is optimized with the objectives of reducing operational cost, capital cost, and carbon dioxide emission (CDE) for different building types and locations. The proposed system, which includes organic Rankine cycle, flat plate solar thermal collector (FPC), and battery energy storage, is configured in such a manner that the solar-powered ORC generates electricity for the building during daytime while the BES stores excess electricity for later use. A multi-objective particle swarm optimization (MOPSO) is adopted to determine the optimal battery size and solar thermal collector size for the proposed ORC-BES system. Several different dry organic fluids are selected to evaluate the performance of the system. The building types under investigation in this paper include hospitals and large offices, which are the commercial reference building models developed by the Department of Energy (DOE). Several locations separated in different climate zones in the United States, including California, New Mexico, Texas, Florida, and Georgia are chosen as case studies to present the optimization results. Results show that the proposed optimization method can be effectively applied to the ORC-BES system to obtain an optimal design and operation, which reaches a trade-off between the economic, energy, and environmental performance of different buildings and locations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".