An efficient hydrogen-based water-power strategy to alleviate the number of transmission switching within smart grid
Bibliographic record
Abstract
The main advantage of Transmission Switching (TS) is decreasing the cost function in a power system. Using TS requires a high number of transmission switching in the system, which can cause some problems in the long run. These problems include decreased lifetime and failure of circuit breakers (CBs), higher repair and maintenance costs, line outage, increased probability of load shedding, and lower reliability of the system. In this paper, congestion management is utilized in the unit commitment problem constrained to the security with the TS to decrease the number of switching. This methodology will resolve the mentioned problems and improve the overall security of the system. Besides, a grid-connected water-power package is suggested to make relaxation for the line congestion which results in the alleviation of the transmission switching. The proposed water-power system is restructured regarding the fuel cell based renewable resource considering the hydrogen tank. Indeed, such a restructured system utilizes the water grid to generate the hydrogen and then power with the aim of linking the electrical grid. Also, on the account of being uncertain of some parameters coming in the electrical grid, an uncertainty-based UT function is addressed to handle uncertainty impacts on the grid's performance. To make awareness-raising, we carry out an outage of the generators as a different contingency scenario of the problem. Finally, the introduced model is testified on two 6-bus and 118-bus grids and solved by Bender's decomposition method. The simulations are performed in GAMS software to confirm the introduced approach effectiveness. Inferred from the results that the proposed strategy can help the grid operator lessen the line congestion up to an acceptable level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".