Field Testing and Economic Analysis of Residential Vehicle to Grid Deployment
Bibliographic record
Abstract
The development of Electric Vehicles (EVs) has surged in recent years, positioning them as direct replacements for fossil fuel-dependent internal combustion engines. However, the rise in EVs will place considerable strain on the grid, necessitating transmission and distribution infrastructure upgrades. In this regard, EVs equipped with bidirectional charging can act as independent energy storage, managing energy at home during normal and emergency conditions, feeding excess energy back into the grid to reduce its strain, and potentially generating revenue for their owners. However, the high cost of Residential Bidirectional Chargers (RBC) and the limited availability of EVs that support bidirectional charging, particularly Vehicle-to-Home/Grid (V2H/G), remain significant barriers. Despite extensive theoretical research on bidirectional charging of EVs, there is a lack of real-world testing and comprehensive techno-economic analysis to assess the feasibility of the wide deployment of these technologies in residential areas. This thesis aims to investigate the techno-economic viability for the wide deployment of RBC throughout the following: 1. Field Testing for RBCs: Conducting practical evaluations of RBCs to gather empirical data on their performance, efficiency, and reliability under real-world conditions. 2. Identification of Policy and Regulatory Barriers: Analyzing the legislative and regulatory frameworks in Ontario to identify obstacles that hinder the deployment of RBCs, and proposing solutions to overcome these barriers. 3. Development of a Mathematical Model: Creating a model to perform a regional-wide cost-benefit analysis for RBC deployment programs, considering three main stakeholders: local distribution companies, EV owners, and ratepayers. By addressing these aspects, this research will provide a comprehensive understanding of the practical and economic implications of implementing RBC technologies, paving the way for its broader adoption and integration into the energy infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".