Design and Analysis of Flexible Multi-Microgrid Interconnection Scheme for Mitigating Power Fluctuation and Optimizing Storage Capacity
Bibliographic record
Abstract
With the rapid increase of renewable energy integration, more serious power fluctuations are introduced in distribution systems. To mitigate power fluctuations caused by renewables, a microgrid with energy storage systems (ESSs) is an attractive solution. However, existing solutions are still not sufficiently cost-effective for compensating enormous power fluctuations considering the high unit cost of ESS. This paper proposes a new flexible multi-microgrid interconnection scheme to address this problem while optimizing the utilization of ESSs as well. The basic structure and functions of the proposed scheme are illustrated first. With the suitable power allocation method in place to realize fluctuation sharing among microgrids, the effectiveness of this scheme in power smoothing is analyzed mathematically. The corresponding power control strategies of multiple converters integrated into the DC common bus are designed, and the power fluctuation sharing could be achieved by all AC microgrids and DC-side ESS. In addition, a novel ESS sizing method which can deal with discrete data set is introduced. The proposed interconnection scheme is compared with a conventional independent microgrid scheme through real-world case studies. The results demonstrate the effectiveness of the interconnected microgrid scheme in mitigating power fluctuation and optimizing storage capacity, while at the expense of slightly increased capacity requirement for the AC/DC converters and construction cost for DC lines. According to the economic analysis, the proposed scheme is most suitable for areas where the distances between microgrids are short.
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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) | 0.001 | 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 | 0.001 | 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".