Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Photovoltaic panels, electric vehicles, and vehicle-to-grid technologies are becoming more common and hold significant promises to improve the grid and foster the energy transition. However, significant questions remain unanswered with respect to who should invest in this equipment and what tariff should be used. This paper examines whether the distribution company or prosumer should invest in and manage Distributed Energy Resources (DER), the ideal combination of DER to utilize, and the appropriate tariff to implement. Central to this analysis is the assessment of different stakeholder objectives, particularly from the investor's perspective, where net present value is used as the primary criterion for evaluating the different investment scenarios. Additionally, the impact of these scenarios on the annual system cost is calculated. A mathematical scenario analysis model is developed to simulate the operation of DER and energy management systems. This model utilizes the Vermont electricity grid's real-world consumption, generation data, and cost structures. The results underscore the significance of incorporating vehicle-to-grid technology to enhance the profitability of DER investments. This inclusion of specific data sources and stakeholder criteria aims to provide insight into the complex dynamics of smart-home deployment.
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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