A simple way to integrate distributed storage into a wholesale electricity market
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator.
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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 | 0.001 | 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 | 0.000 | 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 it