Design and optimization of renewable energy-based electrification and water pumping systems
Bibliographic record
Abstract
In this research, the design and optimization of a renewable energy-based system for the dual application of electrification and water pumping is investigated. The power generated by the renewable system is initially used for electrification purposes, and the excess power is used for water pumping for different purposes, like providing water for a small community or supplying water for irrigation purposes. Two concepts, loss of power supply probability (LPSP) and water shortage probability (WSP) are used for assessing the reliability of the system. In addition, for evaluating the feasibility of the study, various economic parameters, including payback period, CAPEX, and levelized cost of energy, are considered. Two types of energy systems are used in the study: solar photovoltaic (PV)- battery energy storage system (BESS) and PV-wind turbine (WT)- BESS. The first energy system (i.e., PV- BESS) is assumed to be used for a small house with constant energy consumption throughout the year, where the excess power is used for irrigating a farm in southern Iran, a region with abundant solar potential. The second energy system (i.e., PV- WT- BESS) is evaluated for a small community in Quebec with variable electricity consumption throughout the year (considerably higher in cold seasons), where the excess power from the wind turbines and PV panels is used for providing water for the community. A custom algorithm for numerical calculations and a dispatch strategy are employed. In brief, in this dispatch strategy, the priority is to generate electricity and then to charge the bank of batteries. If these conditions are met, the excess power is used for water pumping. The main goal of the study is to show the feasibility of a renewable energy-based system for electrifying and using the excess power for water pumping. Furthermore, this research is aimed at showing how much the user`s tolerance in terms of LPSP or WSP can lower system size and consequently its CAPEX. A multi-objective optimization is conducted by using NSGA-II method to find a Pareto front that satisfies users with various tolerances, and economic objectives. It is worth noting that this study does not disregard the fact that in real-world projects, a conventional system like a diesel generator should be included in the design to account for renewable energy's intermittent nature or maintenance issues. However, this study aims to increase renewable energy penetration for remote communities with limited access to grid electricity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".