Design of a two-renewable energy source-based system with thermal energy storage and hydrogen storage for sustainable development
Bibliographic record
Abstract
A renewable energy-based system, including an energy storage subsystem, is designed within the scope of this proposed study to meet the fresh water and electricity demands of a community. The city of Imperial in the state of California is, in this regard, considered for a case study. The energy storage solutions are incorporated into the system to enhance the flexibility and efficiency of energy systems since solar and wind energy resources fluctuate. These energy storage options help offset the mismatch between demand and supply to provide feasible solutions practically. The current system employs storage technologies, such as thermal energy storage (TES) and hydrogen, to efficiently store excess energy for usage during peak demand periods. The parabolic through solar collector (PTSC) and wind turbine in the system are modelled using the System Advisor Model (SAM). Furthermore, the energy and exergy aspects of thermodynamic are considered when performing the thermodynamic analysis of the entire system. The Engineering Equation Solver (EES) is employed to construct the programming codes and perform a thermodynamic analysis of the overall system and its components. The effects of varying numerous parameters on the system performance are studied in this proposed study using a parametric investigation which is carried out through the changes in the ambient temperature, source temperatures, and mass flow rate of the current system. The results of this study show that the annual net amount of electricity generated by the proposed system is 74.90 GWh, which meets 11.7 % of the residential electricity needs of the Imperial City. The desalination unit in the system is also able to produce fresh water from seawater, meeting the needs of 4708 families. The current system further produces a total of 3379.53 tons of fresh water and 226.98 tons of H2 annually. As a result of the energy and exergy analyses performed for the designed system, the energy and exergy efficiencies for the overall system are found to be 47.5 % and 34.3 %, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".