Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries
Bibliographic record
Abstract
In this article, an innovative approach is presented to the sizing and technical–economic analysis of battery energy‐storage systems (BESS) designed for customers in the free energy market in Brazil. The tool enables the integration of photovoltaic (PV) energy sources and includes a comparison between the BESS + PV system and diesel generators. Integrating the computational capabilities of Microsoft Excel in the backend and the intuitive interface of PowerApps in the front end, the sizing process is based on analyzing historical energy invoices spanning at least 12 months or loading data with a 1 h measurement interval. The data discretization over the 8760 h of the year, considering the consumer's load profile, is facilitated by the PowerApps interface, providing a comprehensive visualization of the technical‐economic sizing results for BESS. A case study is conducted for a commercial load with a specific tariff for free energy market customers, revealing viable solutions for BESS compared to diesel alternatives. In this approach, it is aimed to simplify the analysis and decision‐making process, offering a valuable tool for power system engineers to evaluate sustainable and economically viable solutions in the Brazilian free energy market.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".