Economic and environmental analysis of a green energy hub with energy storage under fixed and variable pricing structures
Bibliographic record
Abstract
With the increased use of intermittent renewable power generation, including wind and solar power, the need for energy storage is increasing. Repurposed hybrid electric vehicle lithium ion batteries have been shown to have energy storage potential at a modest capital cost. In this paper, the authors use a two stage MatLAB simulation to create and optimise, a net zero grid-connected facility. The yearly electricity price is calculated under various scenarios using two different pricing structures, fixed feed-in tariff and market pricing. The facility under consideration is a commercial distribution centre with refrigeration, onsite generation of hydrogen for fuel cell powered forklifts, solar and wind power generation, and re-purposed batteries for energy storage. Importantly, the feed-in tariff mechanism is shown to be a deterrent to implementing energy storage onsite, as well as to increasing the use of locally generated power. Although further savings are possible in the model, when energy storage is used, close to $50,000 in savings can be seen from electrolyser load shifting that requires no capital investment. The mechanism also negatively influences indirect electricity emissions, which is not consistent with environmental objectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".