When Shared Autonomous Electric Vehicles Meet Microgrids: Citywide Energy-Mobility Orchestration
Why this work is in the frame
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Bibliographic record
Abstract
Problem definition: We develop a crossdisciplinary analytics framework to understand citywide mobility-energy synergy. In particular, we investigate the potential of shared autonomous electric vehicles (SAEVs) for improving the self-sufficiency and resilience of solar-powered urban microgrids. Academic/practical relevance: Our work is motivated by the ever-increasing interconnection of energy and mobility service systems at the urban scale. We propose models and analytics to characterize the dynamics of the SAEV-microgrid service systems, which were largely overlooked by the literature on service operations and vehicle-grid integration (VGI) analysis. Methodology: We develop a space-time-energy network representation of SAEVs. Then, we formulate linear program models to incorporate an array of major operational decisions interconnecting the mobility and energy systems. To preventatively ensure microgrid resilience, we also propose an “N − 1” resilience-constrained fleet dispatch problem to cope with microgrid outages. Results: Combining eight data sources of New York City, our results show that 80,000 SAEVs in place of the current ride-sharing mobility assets can improve the microgrid self-sufficiency by 1.45% (benchmarked against the case without grid support) mainly via the spatial transfer of electricity, which complements conventional VGI. Scaling up the SAEV fleet size to 500,000 increases the microgrid self-sufficiency by 8.85% mainly through temporal energy transfer, which substitutes conventional VGI. We also quantify the potential and trade-offs of SAEVs for peak electricity import reduction and ramping mitigation. In addition, microgrid resilience can be enhanced by SAEVs, but the actual resilience level varies by microgrids and by the hour when grid contingency occurs. The SAEV fleet operator can further maintain the resilience of pivotal microgrid areas at their maximum achievable level with no more than a 1% increase in the fleet repositioning trip length. Managerial implications: Our models and findings demonstrate the potential in deepening the integration of urban mobility and energy service systems toward a smart-city future.
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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 it