Optimizing off-grid PV/wind systems with battery and water storage for rural energy and water access
Bibliographic record
Abstract
This study presents a multi-objective optimization framework for improving affordability, reliability, and water access in standalone off-grid energy systems integrating photovoltaic (PV) panels, wind turbines (WT), battery storage, and water reservoirs. The system is designed to meet both residential load demand and water needs. A mathematical model and a tailored Energy Management System (EMS) algorithm optimize power generation, energy storage, and water pumping. The EMS prioritizes residential electricity supply, ensuring battery charging for nighttime and low-irradiation periods, while excess power is used for water storage. Main performance parameters including Loss of Power Supply Probability (LPSP), Water Shortage Probability (WSP), and Capital Expenditure (CapEx) are optimized using a genetic algorithm (GA)-based multi-objective technique in order to enhance reliability, water availability, and cost efficiency of the system. A detailed financial model and reliability analysis evaluate system performance, with a case study in a remote island in Quebec demonstrating the feasibility of an autonomous, off-grid energy solution. The results show that the optimized system could effectively supply residential electricity while utilizing surplus power for water pumping—thus, reducing reliance on diesel generators (DG) or grid electricity. The proposed solutions showed a payback period of 8 to 12 years with LCOE in the range of 16.3 ¢/kWh to 23.4 ¢/kWh. • Tailored EMS optimizes off-grid PV/WT systems with battery and water storage for rural needs. • Multi-objective GA minimizes LPSP, WSP, and CAPEX for reliable, cost-effective solutions. • Case study in Îles-de-la-Madeleine, Quebec shows PV/WT complementarity enhances seasonal reliability. • Optimized system achievable with LPSP = 5 %, WSP = 10 %, and LCOE of 23.3 ¢/kWh. • Economic analysis shows payback period within 9–12 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".