Power-to-gas and power-to-power for storage and ancillary services in urban areas
Bibliographic record
Abstract
In this study, power-to-gas, whereby hydrogen is generated electrolytically, and power-to-power, whereby electricity is used to produce hydrogen which is used, in turn, to generate electricity at a later time, are examined for their efficiency and emissions reductions in providing energy storage and ancillary services. Due to a large baseload of nuclear energy in the province of Ontario, and with wind generation added to the grid prior to 2014, the supply of electricity exceeds demand at certain times of the year during off-peak hours. To manage this excess supply, electricity is exported to neighboring provinces and states at a low, often negative price, due to the decreased demand and surplus generation. To curb these exports, the Independent Electricity System Operator (IESO) has switched renewable generators from nondispatchable to dispatchable energy sources that can be turned on or off or adjusted to output a different quantity of energy. In addition, due to the large baseload, the IESO has also shifted to allowing loads to offer demand response services previously only offered by generators. In this analysis, the rapid response of polymer electrolyte membrane electrolyzers, used to generate hydrogen in power-to-gas and power-to-power systems is also able to offer important and high value auxiliary and regulatory power services. In power-to-gas systems, the hydrogen produced is an alternative energy vector which can be contained within the natural gas infrastructure or other storage medium. For this analysis, the authors employ the General Algebraic Modeling Simulation to develop a simulation of a 2-MW power-to-gas and power-to-power system that produces hydrogen for energy storage and then uses this hydrogen to generate electricity when there is a peak in energy demand. This energy is then reintroduced into the electrical grid using a hydrogen turbine. The power-to-power scheme, although typically less energy efficient, provides the flexibility to meet changing energy demands while generating hydrogen that can be used for industrial purposes and as a transportation fuel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".