On Utilization of Commercial Electric Vehicles for Grid Services
Bibliographic record
Abstract
The penetration of passenger electric vehicles into the grid has seen remarkable growth over the past years. This transition is now extending into the commercial sector, where fleets are shifting from conventional to electric vehicles. The aggregated batteries of these electric fleets can provide a large amount of energy and power. When strategically deployed, these batteries can help fleet owners reduce electricity costs by providing energy during peak hours, facilitating load shifting, or generating revenue by renting battery capacity to third parties or the grid. Therefore, this thesis explores the potential of Electric Commercial Fleets (ECFs) for energy services and examines the benefits they can offer to both fleet owners and the grid. In the first contribution of this thesis, a mutlistage investment planning is developed for the transition from conventional fleet to ECF. This work first analyzes the revenue opportunities within the electricity markets in North America. Next, it assesses how these auxiliary services can influence the dynamics of fleet transitions, reduce the total cost of ownership for fleet owners, and accelerate the transition to electric fleets. Two additional contributions focus on the daily operational aspects of these fleets. In the first contribution of operation planning, a new time-index Vehicle Routing Problem (VRP) is developed that helps commercial and industrial entity to concurrently optimize their logistics and energy provision for their self-use. The advantage of this method is shown compared to the conventional VRP. For larger benchmarks, Column Generation and Branch-and-Price techniques is employed to decompose the large instances into smaller, more manageable problems for efficient solving. In the last contribution, the electric fleet is utilized as mobile battery storage to address planned outages across the city of Calgary. A robust optimization counterpart is further developed to deal with the uncertainties of logistics and electricity demand. The results illustrate that ECFs have the potential to outperform a single mobile battery system both financially and technically. This advantage stems from employing the sharing economy concept and the presence of a larger number of vehicles distributed across the city.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".