Performance and analysis of retail <scp>store‐centered</scp> microgrids with solar photovoltaic parking lot, cogeneration, and battery‐based hybrid systems
Bibliographic record
Abstract
Abstract To examine the potential of distributed microgrids using sustainable energy sources centered on retail store parking lots, this study provides a methodology to simulate medium‐scale solar photovoltaic (PV) + combined heat and power (CHP) + battery hybrid microgrid systems deployed at big box retail stores. First, a method is provided to agglomerate 15‐min load data for a community of residences in the region of the store to provide baseline electricity load profiles. The systems are then modeled using dispatch strategies previously shown to be stable for smaller, but more dynamic loaded systems. The methodology is demonstrated with a case study for a Walmart Supercenter located in Nova Scotia, Canada. The electricity generated by each component of the hybrid system is coupled and optimized to fulfill the electric demands of the local community. The results provide the total electricity generated by the PV + CHP + battery hybrid system, total operating hours by each unit, fuel consumed, and hourly energy produced by each unit. The results show that such microgrid systems could serve the electrical needs of ˜1000 people (350 residences) for each parking lot of 3.5 MW PV system and CHP unit of 1 MW. The technical viability of this approach warrants future work.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".