Effect of price responsive demand on the operation of microgrids
Bibliographic record
Abstract
In this paper, a demand elasticity model is developed and tested for the dispatch of microgrids. The price obtained from dispatching the network in a base-case scenario is used as input to a demand elasticity model; this demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a microgrid can be obtained, to study the impact of demand response on unit commitment. This way, for a microgrid, the effect on the scheduling of diesel generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a benchmark microgrid show that the proposed 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios. Moreover, it is confirmed that elastic demand in a microgrid reduces electricity price variability and mitigates the need for storage in the presence of high penetration of renewable energy.
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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".