Market Power Mitigation in Transmission Expansion Planning Problems
Bibliographic record
Abstract
The exercise of market power through network constraints in electricity markets can lead to high energy prices far from competitive prices. Traditional transmission expansion planning problem formulations do not consider strategic behavior of market agents. Therefore, they cannot capture the potential exercise of market power. In this paper, a predictor-corrector iterative algorithm is proposed to deal with market power mitigation in market-oriented transmission expansion planning problems. The predictor step consists of the solution of an equilibrium market model based on the conjectured supply function. The corrector step is a conventional transmission expansion planning posed as a mixed integer linear programming problem, where the feasible region is dynamically updated taking into account the results from the predictor step. Lerner index and other indices are used to quantify the potential market power. The algorithm finds the minimum cost expansion plan that avoids the exercise of market power through network congestion. The cost of this expansion plan is only slightly greater than the cost of a conventional expansion plan. The approach is illustrated using the 6-Bus Garver and the IEEE-24 RTS test systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".