Photovoltaic Electrification and Water Pumping Using the Concepts of Water Shortage Probability and Loss of Power Supply Probability: A Case Study
Bibliographic record
Abstract
In this paper, a techno-economic investigation of a small-scale solar water pumping system combined with power generation is conducted numerically. Irrigation and power production for a typical small-size citrus farm located in southern Iran is simulated. The system consists of monocrystalline photovoltaic panels (CS3K-305MS, 305 W), absorbent glass material batteries (8A31DT-DEKA, 104 Wh), inverters (SMA Sunny Boy 2.0, 2000 W), and a pumping storage system. The key concepts of water shortage probability (WSP) and loss of power supply probability (LPSP) are used in conjunction with users’ tolerances and sizing of the system. A genuine MATLAB code was developed and validated before the simulations. A specific electricity consumption pattern for a rural home and a variable irrigation water profile were considered. The main objective of the study is to size a system that provides both electricity for domestic use of a home as well as the energy required for running the irrigation pumps with respect to investment cost, LCOE, WSP, and LPSP. The main findings of the research are that LPSP and WSP threshold tolerances can have a preponderant effect on the cost and sizing of the system. Interestingly, results reveal that there is a minimum variation of the capital expenditure (CAPEX) versus the number of PV panels. For the optimal configuration, the study indicates that shifting from an LPSP of 0% to 3% (or about ten days of potential yearly shortage) makes the LCOE drop by about 55%, while the WSP decreases by about 36%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".