System integration and grid simulation for ancillary services in V2X (Vehicle-to X: X=home, buildings and grid) applications
Bibliographic record
Abstract
V2X (Vehicle-to-X: X=Home, Buildings and Grid) testing facility has been built at the Canadian Centre for Housing Technology (CCHT) Flexhouse for small-scale testing up to 10kW. The CCHT is jointly operated by National Research Council (NRC), National Resources Canada (NRCan), and Canada Mortgage and Housing Corporation (CMHC). The integrated V2X testing system is comprised of a 15kVA grid simulator, dc-dc coupling-based integrated 2kW PV and Li-ion batteries with a 5kW inverter, off-board bi-directional EV charging, V2X-capable EV, house load with Z-wave metering, load simulators, and power analyzer for data acquisition. Raw metering data from the above-mentioned integrated V2X system with PV renewables can be collected to evaluate the reduction of local peak-power demands, access grid service value potential, and perform simulation and validation of proposed energy management and control strategies in order to verify potential benefits of V2H, V2B, and V2G. This unique facility enables NRC to demonstrate EV batteries and repurposed EV batteries in Vehicle-to-Grid and stationary applications and to evaluate its implementation in the Canadian environments and electricity markets. The energy stored in electric vehicles (EVs) would be made available to commercial buildings to actively manage energy consumption and costs in the near future. These concepts known as vehicle-to-building (V2B) and vehicle-to-grid (V2G) technologies have the potential to provide storage capacity to benefit both EVs and buildings owners respectively, by reducing some of the highest cost of EVs, buildings’ energy cost, and providing reliable emergency backup services. In this study, we considered a V2B/V2G storage system simultaneously for peak shaving and frequency regulation via a combined optimization strategy which captures battery state of charge (SOC), EV battery degradation, EV driving scenarios, operational constraints and uncertainties in building load, V2B/V2G patterns and regulation signals. Under these assumptions, we showed that the electricity usage/bill can be reduced. A multi-objective control policy is described and shown to achieve a considerable performance. Comparative analysis with previous works that used battery storage systems for either peak shaving or frequency regulation showed that EV batteries can also achieve superior economic benefits under controlled SOC limits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".