Public transit electrification planning with energy storage via second-life batteries : a stochastic programming approach
Bibliographic record
Abstract
As part of decarbonization strategies, public transit systems are aiming to electrify their fleets in response to climate targets and net-zero goals. However, the resulting increase in electricity demand may lead to energy stress on the electrical grid. Second-life batteries (SLBs) offer a potential solution, yet their financial, energy, and environmental impacts remain underexplored, as does the long-term planning for their integration. This study proposes a strategic planning model for transitioning a public transit fleet to battery electric buses (BEBs), incorporating the deployment of SLBs as a battery energy storage system (BESS). The model jointly optimizes decisions on asset procurement, replacement, route-level fleet assignments, the integration of SLBs as BESS, and the installation of a supporting renewable energy system (RES). A multi-period stochastic programming framework is employed to optimize planning under uncertainties, such as vehicle and battery costs, and the model is formulated as a mixed-integer linear program. A case study of Metro Vancouver’s transit system is conducted to evaluate three electrification pathways. Results show that SLBs can meet up to 84% of the fleet’s recharging energy demand, reduce annual operating costs by up to $107 million, and lower total system costs by $78 million. Spatial constraints on SLB deployment, layout design, and BESS fixed costs may increase the total cost, despite potential savings from BESS operations. Policy incentives to utilize SLBs and reduce BESS installation costs, such as BC Hydro's energy storage incentive, play an important role in the economic viability of the approach. A sensitivity analysis of battery and electricity prices provides insights into the integration of SLBs under different market and policy conditions, with environmental benefits evident for jurisdictions where electricity is produced by non-renewable sources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".