Residential Battery Energy Storage Sizing and Profitability in the Presence of PV and EV
Bibliographic record
Abstract
Residential battery energy storage systems (BESS) are having an important role in transitioning towards low carbon communities. However, BESS capital cost remains questionable. In this work, an optimization-based BESS sizing algorithm is developed to maximize the customer’s profitability by minimizing the electricity import for 162 combinations of demand, solar photovoltaic (PV) and electric vehicle (EV). The sizing algorithm results are utilized in introducing empirical formulas that ease the determination of the near-optimal BESS capacity. The annual savings are quantified for various cases under different tariff rates and the BESS viability is investigated for new installations over the next 10 years. The results show that the residential BESS can be profitable in the UK starting from 2024 without any subsidy for the time of use tariffs.
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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) | 0.000 | 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".