Bibliographic record
Abstract
From homeowner and investor perspective, integration of renewable energy with battery energy storage system (BESS) can have economic benefits. This paper presents the results of battery control in residential microgrid system using photovoltaic (PV) distributed energy resource (DER) in residential applications in Ottawa, Ontario. The battery flow is controlled on an hourly basis to optimize the cost saving from PV microgrid system under net metering and no-feedback utility policies. PV and battery sizes are designed with fixed budgets in a particular price year to match average annual residential electricity power consumption (kWh) and dollar amount of consumption (ĆD) in Ontario. A linear programing (LP) model is developed for PV microgrid system to solve for different scenarios, varying in different price years, net metering and no-feedback utility policies, combinations of different PV-battery size. The LP solution under net metering policy shows the battery flow is only dependent on time-of-use (TOU) rate, while solution under no-feedback policy depends on both TOU rates and utility supply and demand in the microgrid system. In addition, higher financial benefit is realized with larger battery size in the latter price year.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".