Levelized Cost Analysis of Second-Life and New Lithium-Ion Batteries in Microgrid Applications
Bibliographic record
Abstract
The need for sustainable battery energy storage alternatives has led to the exploration of second-life electric vehicle batteries for microgrid applications. This study evaluates the levelized cost of storage for second-life and new lithiumion batteries in microgrids, considering both scenarios with and without electric vehicle integration. A mixed-integer linear programming optimization model is developed to minimize the total electricity cost of microgrids while calculating the optimized size of battery energy storage. The net present value approach is used to account for future costs, including operational costs, investment costs and replacement costs, ensuring a comprehensive economic analysis over the complete planning horizon. Simulations were performed for three microgrids in British Columbia, Canada. Results show that second-life batteries require 50 % more replacements than new lithium-ion batteries over the planning horizon. However, second-life batteries have a$\mathbf{5 0 \%}$lower investment cost as compared to new batteries. The optimized battery sizes reflect the need for larger capacity of second-life batteries to compensate for performance degradation. Furthermore, the levelized cost of storage analysis shows that second-life batteries have up to 30 % higher levelized cost of storage compared to new lithium-ion batteries which makes them less cost-effective over time. Levelized cost of storage increases with the integration of electric vehicles for both battery types, with up to a 10.5 % rise for new lithium-ion batteries and up to an 11.1 % rise for second-life batteries. Contrary to the popular belief, the findings highlight that new lithiumion batteries perform better in the long-term and emerge as a more economical option due to their higher efficiency, increased maximum and minimum state-of-charge, longer lifespan, and lower overall levelized cost of storage.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".