Framework for optimizing the demand contracted by large customers
Bibliographic record
Abstract
Large customers in many electric distribution utilities must enter into demand contracts for the ensuing year for defining contracted demand. Customer demand charge equals contracted demand billed at contracted tariff if the peak demand is less than the contracted demand, and, if not, the excess is billed at the uncontracted tariff. Both scenarios lead to economic loss for the customer, as the uncontracted tariff is much higher than the contracted tariff. Further, optimization of demand contracts is also important for utilities, as they plan and operate their system to satisfy customer peak demand. If under planned, it leads to technical challenges, and otherwise, it leads to economic loss. This challenge of determining the best demand to be contracted is known as the demand cost optimization problem and would save US$ 38 billion globally to customers. This work describes the problem through a graphical approach and proposes three mathematical models to find the optimum demand even in the presence of intermittent renewable generation. Each model is verified through a case study and an exhaustive study with 7,000 large customers from a Brazilian utility. The formulations are easily implementable and have the potential to assist large customers and utilities with planning studies.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".