A Custom Automated Bidding Solution for Short-Term Operation in Mibel Market
Bibliographic record
Abstract
The automation of bidding processes in energy markets has seen significant evolution in recent years. Initially, market participants used manual methods to calculate and submit bids. However, with advancements in technology, the industry began to adopt automated systems to expedite these tasks. This paper describes the implemented solution at SU ELETRICIDADE to automate the bidding process for short-term operation in Mibel Market (SUTRADE). SU ELETRICIDADE, a subsidiary of EDP S.A., is a regulated Portuguese company who operates as the Last Resort Supplier, with the obligation to provide universal electricity supply services and performs the role of Last Resort Aggregator, purchasing energy from producers, namely to the ones that benefits from guaranteed remuneration schemes (feed-in tariffs), and subsequently selling this energy on the market. SU ELETRICIDADE is involved in promoting renewable energy, with wind energy generation making up a large part of its selling energy portfolio. Given the challenges of forecasting wind generation, it is essential for SU ELETRICIDADE to actively participate in intraday markets, submitting updated forecasts to minimize imbalances. For this purpose, a custom robust solution that automates the bidding process is essential. While there is a wide variety of commercial solutions available for automating bidding processes in energy markets, some of which include algorithmic trading features, SUTRADE is a custom, in-house developed solution based on a cloud platform. The primary goal of SUTRADE is to automate manual and repetitive tasks such as calculating bid prices, determining energy volumes, and submitting bids to OMIE, ensuring accuracy and timely submission. Follows a set of predefined rules and parameters, which users can control and adjust according to market conditions. It also features an advanced energy analytical forecast module and an alarmistic setup that proactively alerts users to significant changes in SU ELETRICIDADE's market operations or potential errors caused by unexpected events such as bidding errors or communication issues. SUTRADE allows SU ELETRICIDADE to reduce market participation operational risks, while achieving substantial cost savings by minimizing the need for manual trading activities
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".