Price-Maker Bidding and Offering Strategies for Networked Microgrids in Day-Ahead Electricity Markets
Bibliographic record
Abstract
This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".