A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: A case study for Namin, Iran
Bibliographic record
Abstract
An optimization model is developed to determine the most advantageous size of autonomous hybrid photovoltaic/wind turbine/fuel cell, wind turbine/fuel cell and photovoltaic/fuel cell systems for electrification of a remote area involving five homes (1 block) located in Namin, Ardabil, Iran. The model is developed based on three decision variables related to the system renewable energy components: number of storage tanks, total swept area by the rotating turbine blades and total area occupied by the set of photovoltaic panels. The former is an integer decision variable, while the latter two are continuous decision variables. All the components are modeled and an objective function is defined based on minimizing the life cycle cost and satisfying the maximum allowable loss of power supply probability. To determine optimal values of the variables that satisfy the load in the most cost-effective way, the use of simulated annealing and a combination of simulated annealing with harmony search and chaotic search is proposed. The simulation results indicate that the grid-independent hybrid photovoltaic/wind turbine/fuel cell system is the most cost-effective for supplying the block's electrical energy demands and that the simulated annealing-based harmony search algorithm yields more promising results than the other algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".