Jurisdiction-based optimisation of BESS operating with solar arrays for energy arbitrage
Bibliographic record
Abstract
A battery energy storage system (BESS) can help improve distribution networks in two ways. First, on the technical side, it can support grid operations by providing voltage or frequency regulation, and help in a black start following a major fault. Second, on the economic aspect, BESS can be employed to help create profits through energy arbitrage (EA) and a reduction in various utility charges. In this paper, a planning study is proposed for determining the size and scheduling of the BESS in order to generate profit from EA. The study considers three different data sets as well as rules and regulations from three different jurisdictions throughout the world, namely, Ontario, Canada, Queensland, Australia, and New York West, USA, to investigate the potential of EA and to optimise the size and operation profile of the BESS. The results demonstrate the economic feasibility of employing the BESS for EA in each jurisdiction and show the optimal sizing and scheduling of the BESS in each case.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".