Assessing the Rate Impact of Conservation and Demand Management: A New Mathematical Model
Bibliographic record
Abstract
While numerous studies examine the casual relationships of electricity rates on conservation and of conservation on variable costs, little work has been done on the missing links of conservation on fixed costs and on overall electricity rates. This paper presents a new model to scientifically quantify these two gaps and complete the economic picture of conservation. This knowledge can equip government policymakers and conservation program designers at utilities to create more efficient and effective programs. New mathematical models of the full systems-level rate impact of different forms of conservation are presented. Four common forms of conservation were analyzed for their impacts on total fixed and variable costs and rates: peak shaving (S1); off-peak reduction (S2); peak shifting (S3); and time independent conservation (S4). These were all measured against the base case without conservation (S5). A test system based on the electricity system in Ontario, Canada was created and analyzed over a 21-year period. The results show that different forms of conservation have different impacts on fixed and variable costs and rates, and the most useful metric for the economic impact of conservation is the change in utility rates, inclusive of fixed and variable components. For conservation programs to lower rates, they must decrease the peak demand, which will lower fixed costs by deferring capital investments, and increase utilization, which will lower rates by increasing consumption during off-peak times.
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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 | 0.005 |
| 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 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".