Enhancing Renewable Energy Use in Residential Communities: Analyzing Storage, Trading, and Combinations
Bibliographic record
Abstract
Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to maximize the consumption of renewables in communities encompassing dwellings both with and without PV installations. The three cases considered in this study are local energy storage, community energy storage, and internal trading. A total of six cases are analyzed by evaluating these cases individually and in combinations. To achieve this, first, a generalized optimization model with specific constraints for each case is developed. Subsequently, different indices are devised to quantitatively measure trading with the grid and the consumption of renewables under varying cases. The performance of these different cases is analyzed for a community comprising five dwellings over a summer week. Furthermore, the performance of each case is evaluated for various seasons throughout the year. Additionally, a sensitivity analysis of different storage capacities (both local and community) is conducted. Simulation results indicate that community storage results in the highest renewable consumption if only one case is considered. However, the overall combination of internal trading and community storage results in the highest cost reduction, lowest dependence on the grid, and the highest consumption of renewables. Finally, a techno-economic analysis is performed on four widely used battery technologies, taking into account diverse cost and technical considerations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".