A novel application of ACO to price transmission rights in electricity markets
Bibliographic record
Abstract
Financial risk management is of high priority for participants in wholesale deregulated electricity markets due to substantial price and volume risks. Due to high complexity of a wholesale electricity market, prices can exhibit high volatility at times of peak demand and supply shortages. Electricity markets are partly dependent on characteristics such as generation, demand, weather patterns etc. Transmission Rights (TR or TRs) are designed to provide a financial hedge for markets participants in a deregulated electricity market. TRs are financial instruments that entitle the holder to a stream of revenues based on the hourly congestion price differences across a transmission path. The dynamic change in electricity prices poses greater challenges to price and compute payouts from TRs. We have observed various similarities between the TRs and Options (derivatives) in financial market and few differences as well. As a first step, we model TR as an option pricing problem. Then, we use a nature inspired meta-heuristic algorithm, Ant Colony Optimization (ACO) to compute option prices and determine future Transmission Rights payouts. Our work suggests that ACO searches computational space eliminating areas that may not provide a profitable solution. Computational time using ACO is lower compared to searching whole solution space exhaustively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".