Return on Investment Evaluation and Optimal Sizing of Behind-the-Meter Battery Energy Storage Systems in Large Commercial Buildings in Ontario
Bibliographic record
Abstract
In Ontario, Canada, electricity in large commercial buildings is charged depending on energy consumption, peak demand, and global adjustment (GA). Installing a behind-the-meter battery energy storage system (BESS) can reduce energy bills for these consumers by: 1) shifting consumption from the high to the low energy price; 2) reducing the peak demand; and 3) mitigating or eliminating the GA charge. In this context, this paper presents a method to estimate the return on investment (ROI) and determine the optimal size of BESS in large commercial buildings in Ontario. The paper’s main contribution is a solid linear model that simulates the BESS optimal operation, complying with the technical characteristics of the BESS, considering Ontario electricity rates and pricing to maximize the ROI from energy arbitrage, peak shaving, and GA busting. Precise formulations for computing the energy bills of large consumers are developed, allowing realistic estimates for the ROI and sizes of BESS. Tests considering two customer classes demonstrate the differences between these classes and the factors that influence the economic returns of BESS for each.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".