The Effect of Fuel and Storage System Price on the Economic Analysis of Off-grid Renewable Energy Systems
Bibliographic record
Abstract
Carbon emissions mitigation is driving the need to decarbonize different energy systems. Alongside the energy systems decarbonization, there is uncertainty over determining the best goals in terms of cost and emissions. In this work, a hybrid energy system which consists of renewable energy systems, storage systems and a diesel generator are considered to supply the energy demands of an off-grid house. One of the main challenges in off-grid systems is the trade-offs between energy storage and importing diesel. This challenge is due to the variability of both renewable energy resources and the building demands. This paper introduces an energy hub model that is used for the optimal sizing and operation of an energy system. Four scenarios are considered to decide how well an off-grid system works in term of its total cost and greenhouse gas emissions. Our results show that hybrid systems are 35% cheaper (over a 25 year lifespan) than the base case using a diesel generator. This situation gets worse at higher diesel prices, and is helped by lower PV and battery prices, but not in a linear manner. This is illustrated using contour plots that show the impact of different combinations of variables.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".