Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load
Bibliographic record
Abstract
Due to intermittency in renewable energy sources (RES), increase in global warming, and load demands, optimal sizing of distributed energy resources and energy storage is critical for creating a green, cost-effective, and efficient energy system. This research proposes a comprehensive multi-energy microgrid (MEMG) with a hydrogen network to meet the electrical, thermal, and cooling loads. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. Furthermore, an improved resilience backup mechanism for EVs is formulated utilizing hybrid storage. To this end, a mixed-integer linear programming-based optimal sizing model is developed to minimize the annual system cost (ASC) of the MEMG. Moreover, the stochastic parameters are modeled utilizing Monte Carlo Simulations (MCS). The results show that the optimal MEMG along with the hydrogen network reduces the cost by 4\% and emission by 40\%. Furthermore, it is shown that the EVs have a significant impact on ASC, depicting their critical role in the planning and operation of the MEMG. Finally, utilizing hybrid storage backup for resilience load is more suitable as it results in the least ASC by maintaining almost 31.46\% of the resilience load in the form of hydrogen.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".