Solar Microgrid Implementation in Prince George’s County, Maryland
Bibliographic record
Abstract
This paper discusses the benefits associated with developing a solar microgrid in a low-income community in Prince George’s County, Maryland. The benefits include reduced air pollution in the community, reduced adverse health impacts from air pollution, reduced spending on utility bills, as well as increased energy security and a more equitable distribution of renewable energy. Using various sources including reports, academic articles, and case studies, this study proves installation of a microgrid in the County would benefit the community and the surrounding area. An in-depth cost-benefit analysis proves the economic feasibility of a microgrid, and the social benefits provide a sound argument for the benefits of installation. Barriers to implementation are also discussed, focusing on problems related to the source of initial funding. The study concludes with two recommendations for implementing resilient solar photovoltaic systems in Prince George’s County. First, finding alternative funding for a microgrid such as federal grants, public partnership, private sector involvement, and community-based funding. Second, the County should consider using community solar rather than a microgrid based on case studies that indicate the cost-effectiveness and increased feasibility of community solar compared to a solar microgrid.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".