Can an EV storage system exercise market power in an electricity market? A comparison of bi-level and single-level models
Bibliographic record
Abstract
An EV aggregator managing multiple EV garages is a distributed EV storage system that can play a significant role in the electricity market of the area where it sits. We analyze the ability of such an EV storage system to exercise market power. For this, we use two models. The first one represents the EV storage system as a price maker, and the second one allows the system operator to freely operate the EV storage system for the best of the power system as a whole. Considering analytical outcomes from a stylized model and numerical simulations from a realistic case study, we found that the EV storage system has limited ability to exercise market power. Thus, the outcomes of the price-maker model and those of the model in which the system operator is in control of the EV storage system are generally similar. This is so for a number of reasons, namely, (i) the EV storage system needs to charge prior to producing (discharging), (ii) its energy/power capacity is comparatively small, (iii) it is generally located at a single node of the power system and (iv) the step-wise supply curve of most power systems is increasingly flat.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".