Feasibility and optimization of hybrid energy systems for sustainable electricity, heat, and fresh water production in a rural community
Bibliographic record
Abstract
Addressing the simultaneous provision of electricity, heat, and water in rural areas poses a significant global challenge. This study optimizes a poly-generation hybrid energy system (HES) integrating diverse energy sources with desalination, tailored for warm climates, targeting Sar Goli village and a health clinic in Khuzestan province, Iran. Option I, integrating 51.2 kW of PV and 10 kW of wind turbine (WT), is the most cost-effective choice with a net present cost (NPC) of $207,203 and a cost of electricity (COE) of $0.161/kWh. In contrast, Option IV, relying primarily on 490 kW WT without PV, is the least economically advantageous with an NPC of $1,012,129 and a COE of $0.789/kWh. Option II (PV-BLR-BT-CNV) shows the highest electrical production of 229,336 kWh/year and complete reliance on renewables, despite a higher loss of power supply probability (LPSP) of 0.0006. Higher solar irradiation and wind speeds reduce NPC and COE, while rising diesel prices increase economic challenges, emphasizing the need for strategic planning. Enhancing boiler efficiency cuts fuel consumption from 509 to 170 liters/year and CO2 emissions from 1,018 to 340 kg/year. Changes in the battery’s minimum state of charge (SOCmin) significantly impact HES finances and reliability; optimizing SOCmin minimizes NPC and COE while ensuring system stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".