Sizing versus Price: How they influence the energy exchange among large numbers of hydrogen-centric multi-energy supply grid-connected microgrids
Bibliographic record
Abstract
Hydrogen-centric multi-energy supply microgrids cover different types of energy, such as electricity/heat/gas/hydrogen, which will play an important role in emerging smart cities. On the other hand, multi-energy supply microgrids can also exchange energy with utility grid networks, which can help the local consumers earn profits, and also resist natural disasters. However, to efficiently achieve energy exchange among regional multi-energy supply grid-connected microgrids is still a complex problem, especially considering that there are large numbers of hydrogen-centric microgrids, and multiple types of exchanged energy (electricity, heat, gas, hydrogen). In this paper, we evaluate how the sizing and price influence the energy exchange among large numbers of hydrogen-centric multi-energy supply microgrids. First, we presented a hierarchy structure to manage exchanged energy, namely, utility grids-load service entity (LSE)-microgrids. Second, we use the prices as the only guide to encourage different parts to achieve energy exchange. And the price-based optimal operation strategy of microgrid and LSE is developed. Third, an extended model with an IEEE30+Gas20+Heat14 hybrid utility grid network, 4 LSEs, and 16 hydrogen-centric multi-energy supply microgrids are built. Last, different sizing and price profiles are deployed. The simulation results show that large sizing indicates large earned profits while large price presents small profits. And through the hydrogen, invisible power can be stored in tanks, and can be further exchanged at any time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".