Identification of ESS Degradations Related to Their Uses in Micro-Grids: Application to a Building Lighting Network with VRLA Batteries
Bibliographic record
Abstract
This paper presents a study about power profiles of micro-grid with highly intermittent sources and their impacts on energy storage system (ESS). The first step of the work consists in generating the ESS power profiles thanks to a new optimal sizing algorithm. Our approach allows to size the ESS and the renewable energy sources (RES) using a power/energy considerations to generate charging and discharging profiles regardless ESS specifics parameters. In a second step, we review the potential damages on Valves Regulated Lead Acid Batteries (VRLAB). This technology has been chosen because it is the most used ESS in case of stationary applications for urban MG with RES integration. We propose some criterion to quantify the batteries stresses generated by MG working operations. Therefore, we give recommendations to enhance the VRLAB lifetime in both micro-grid design and energy management. Our method has been applied to the photovoltaic production and lighting network consumption profiles of the LAAS-CNRS building integrated photovoltaic. We compare four possible configurations of ESS and RES: two determined thanks to Pareto optimisation method and two critical cases corresponding to the minimal and the maximal values of ESS size into all the possible configuration tested.
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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 | 0.002 |
| 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".