Optimal Bidding Strategy in Day-Ahead Electricity Market for Large Consumers
Bibliographic record
Abstract
Electricity price forecasting has become an essential task for electricity buyers and sellers participating in competitive power markets. Compared to the existing point forecasting methods, probabilistic forecasting approaches provide more information about the future forecasts of electricity prices. This paper uses the double exponential smoothing (DES) and triple exponential smoothing (TES) methods to compute volatility forecasts of day-ahead electricity prices. Moreover, we use the elastic net regularization to compute regularized forecasts for volatility. Sample sign correlation of standardized electricity prices (standardized by volatility forecasts) is used to identify the conditional distribution of the electricity price series. Validation of the regularized volatility forecasts is demonstrated using the publicly available hourly Ontario electricity prices. Our data analysis shows that TES forecasts of volatility outperform DES forecasts. Besides, elastic net regularization decreases the mean absolute error of the TES day-ahead volatility forecasts from 11.98 to 11.07. Energy procurement of a large consumer is modelled as an optimization problem to find the optimal bidding strategy. First, a gradient boosting regression (GBR) method computes the optimum electricity prices to be placed in the day-ahead market. Then, a linear programming method is used to obtain the optimum strategy by computing the quantities that need to be bought from the market for the upcoming day. Our simulation results indicate that using probabilistic forecasts of electricity prices leads to a more flexible and efficient bidding strategy than using the point forecasts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".