Assessing the Rate Impact of Conservation and Demand Management: A New Mathematical Model
Bibliographic record
Abstract
While numerous studies examine the causal 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 article 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 system-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 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".