Microgrid systems in U.S. energy infrastructure: A comprehensive review: Exploring decentralized energy solutions, their benefits, and challenges in regional implementation
Bibliographic record
Abstract
This study presents a comprehensive review of microgrid systems within the U.S. energy infrastructure, focusing on decentralized energy solutions and their regional implementation. The primary objective is to explore the evolution, current state, and future prospects of microgrid technologies, assessing their technological, economic, and environmental impacts on regional energy infrastructures. Employing a systematic literature review methodology, the study synthesizes data from peer-reviewed journals, industry reports, and government publications. The search strategy involved keyword searches and manual screening, adhering to strict inclusion and exclusion criteria to ensure relevance and contemporary significance. Key findings reveal that microgrids are instrumental in enhancing energy security, integrating renewable energy sources, and providing economic benefits through decentralized solutions. Technological advancements in microgrid components and control systems have significantly improved efficiency and adaptability. However, challenges such as regulatory hurdles, technological integration, and financial constraints persist. The study concludes that microgrids represent a transformative solution for the future of energy systems, balancing reliability, sustainability, and economic viability. Strategic recommendations for industry and policymakers include developing clear regulatory frameworks, investing in advanced storage solutions, and incentivizing renewable energy integration. Future research directions emphasize enhancing microgrid interoperability with traditional grids, developing robust cybersecurity measures, and exploring innovative business models. In summary, microgrids stand at the forefront of revolutionizing the energy sector, offering a path towards a more resilient, sustainable, and equitable energy future, with ongoing advancements shaping the energy systems of tomorrow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".