Improving Performance and Scalability of Onsite Renewable Energy Using Building-Level Microgrids
Bibliographic record
Abstract
Onsite renewable energy systems are important components of sustainable buildings due to their ability to offset increasing electricity demand, aid in grid decarbonization, and provide energy resilience at the point of consumption. However, typical approaches to design and integration often fail to capitalize on these potential benefits and are ultimately limited in achievable penetration due to a variety of barriers. In this work, building-level microgrids are assessed as an alternative integration mechanism for onsite renewables, which may overcome many barriers to implementation. Key design variables including load segmentation, battery control and generator diversification are evaluated in a simulation-based comparative analysis of performance across grid integration and resilience. Results show that isolated microgrid topologies improve the grid integration performance of a building with onsite renewables across metrics including peak load, ramp rates and generation multiple. It is also shown that renewable microgrids can be leveraged to provide reliable energy resilience, and battery control strategies employing predictive charging and adaptive reserve capacity management can enhance energy resilience with limited storage capacity. Ultimately, the work suggests that the design focus and capital allocation for renewable energy systems in buildings should shift from purely net-zero energy targets, towards delivering more holistic benefits to both the building occupants and the broader power system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".