Optimal Selection and Operation of DER in Microgrids, Analysis of Hydrogen for Transportation and Stationary Applications
Bibliographic record
Abstract
The current energetic context, characterized by the need to reduce fossil fuels, can be overcome by the adoption of Variable Renewable Energy Sources (VRES) such as wind and solar. However, due to its variability, its expansion imposes challenges in current power systems. Emerging technologies such as microgrids can improve the integration of VRES and for that, Energy Storage Systems (ESSs) are fundamental. Hydrogen is being recognized as an ESS (H2ESS) which can be produced sustainably by water electrolysis using VRES excess, having as main advantage, the possibility to be used in several applications ranging from its conversion into electricity to direct use in industry and transportation. The H2ESS adoption is restricted mainly by the high investment costs. In this sense, a modeling approach is adopted in this work to analyze the feasibility of H2ESS in microgrids to supply transportation and electricity stationary loads. A MILP model is implemented in GAMS to solve an optimization problem to determine the optimal portfolio and optimal dispatch. The main results indicate the environmental benefits of H2ESS but the several required devices and processes make the total annual cost with H2ESS greater than using other ESS such as batteries. Scenarios with cost reduction prospects for H2ESS confirm its environmental benefits and make its economically competitive compared to batteries and scenarios without investment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".