Using OCPP for Data Collection in BC Hydro Time-of-Use Measurement Trial for Residential EV Charging
Bibliographic record
Abstract
Rapid adoption of electric vehicles has introduced new challenges to power utilities by increasing demand on distribution systems. To avoid costly system reinforcements, power utilities can reduce adverse grid impacts and excessive costs for the customers using time-of-use pricing or smart charging programs. Proper infrastructure and data acquisition methodology are the necessities of such program. The data acquisition methodology should be accurate, secure, and economically viable. Different methodologies have been introduced and utilized during the past few years that may not be economically viable or may not provide acceptable accuracy and security levels. In this paper, OCPP has been used to transfer measured data from onboard meters of OCPP-enabled AC Level 2 chargers used in a BC Hydro TOU measurement trial for residential EV charging. The accuracy of the collected data from different chargers has been verified against a utility-grade smart meter. It is shown that the measured data is within a reasonable accuracy range, and it is transferred and stored securely without loss of accuracy. Finally, the collected EV load data is assessed over a period of 24 hours to demonstrate the impact of TOU on the total household load and the importance of proper scheduling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".