Fuel efficiency and economic assessment of a hybrid power supply system for mission critical applications
Bibliographic record
Abstract
Hybrid power supplies leveraging renewable energy sources have emerged as pivotal solutions ensuring uninterrupted power for critical applications like telecom towers in remote regions. However, limited research has evaluated the real-world performance, fuel efficiency and economic viability of commercially deployed systems particularly those using liquid propane (LP) as the primary fuel source. This paper evaluates the feasibility and efficacy of a hybrid power supply integrating a LP generator, Battery Energy Storage (BES) and Photovoltaic Panel (PV). Three configurations—LP only, LP-BES and LP-BES-PV are assessed using a spreadsheet based simulation across multiple loading conditions and geographic regions, including Canada, Nigeria and Kansas City. Results show that integrating BES and PV can reduce annual fuel consumption by over 55%, significantly lowering operating costs and greenhouse gas emissions. A 20-year total cost of ownership (TCO) analysis demonstrates that hybrid configurations can achieve up to 32% cost savings compared to LP only systems. Environmental impact is quantified using EPA emission factors, revealing that the hybrid system can avoid more than 65.8 metric tons of CO2 annually. Sensitivity analysis further examines the impact of fuel prices, solar energy output and battery costs on system performance. The findings underscore the operational and environmental benefits of hybridizing LP based systems with renewable technologies. While LP based systems offer unique advantages for remote deployments, such as fuel stability and ease of storage, this study confirms that integrating PV and BES significantly enhances performance and long-term cost-effectiveness.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".