Optimal Day-Ahead Scheduling of Power-to-Gas Energy Storage and Gas Load Management in Wholesale Electricity and Gas Markets
Bibliographic record
Abstract
Power-to-gas (PtG) energy storage converts electricity to hydrogen or synthetic natural gas. The gas produced is stored and converted back to electricity at a later time; or it is directly used to supply a gas load and/or sell in the gas market. In the first case, due to double energy conversion in a relatively less efficient process, a large portion of the energy is wasted. The latter case is examined in this paper, where PtG storage is optimally scheduled to convert waste/inexpensive electricity to synthetic natural gas for some useful operations at appropriate time periods. To that end, a new model is proposed for optimal day-ahead scheduling of PtG storage and gas load management in electricity and gas markets to minimize the cost of gas consumption for the gas load. A gas demand forecasting algorithm is integrated into the scheduling model. Reserve provision is formulated as part of the optimization problem to optimally manage the gas load in case of an outage in the gas grid. The application of the proposed model to a test case is examined, and the results are studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".