Demand Charges Minimization for Ontario Class-A Customers Based on the Optimization of Energy Storage System
Bibliographic record
Abstract
Demand charges (DC) is one of the major utility charges especially in the case of large electricity customers. The Energy Storage System (ESS) can be optimized to minimize these charges. For instance, a large consumer can minimize his DC by incorporating an ESS that charges during his low-consuming hours and discharges during his high-consuming hours. In other words, the ESS can shave the high peak powers of the customer's load profile to ensure lower DC. In this way, the large electricity customer will be able to save a big portion of his/her energy bill of which the DC is a part. This paper presents an optimization formulation for the sizing and scheduling of the ESS to minimize the energy monthly bill through the minimization of DC. Based on the market regulations of Ontario (Canada), this study investigates the potential of using the ESS for DC minimization based on real data for a large class-A Canadian electricity customer in Ontario. The results demonstrate the effectiveness of the proposed ESS deployment algorithm in minimizing the overall energy bills of the class-A customer.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".