Integration of a Solar Energy System at the Future Building Laboratory of Concordia University: Simulation, Analysis, and Testing of Different Operational Modes.
Bibliographic record
Abstract
This research explores the development and integration of a solar photovoltaic (PV) system and a vehicle-to-home (V2H) backup power system at the Future Building Laboratory (FBL) of Concordia University, Montreal, Quebec. The study addresses the challenges of rural electrification, proposing renewable energy systems as sustainable and eco-friendly solutions for off-grid applications. A modified electrical power system is designed for the FBL, incorporating critical loads, a subpanel, manual transfer switches, grid-forming and grid-following converters, battery storage, and an electric vehicle with a vehicle-to-home inverter. The system's performance is analyzed through simulation and experimental testing. Sunny Island (grid-forming/battery charger) and Sunny Boy (grid-following) were simulated in both standalone and grid-connected scenarios, with control strategies ensuring seamless operation in grid-forming and grid-following modes. Experimental results validate the simulations, demonstrating consistent performance of the converters under different operating conditions, including parallel operation. The research also explores vehicle-to-load (V2L) and vehicle-to-home (V2H) systems, highlighting the capability of EVs to serve as reliable emergency backup power sources. The experimental results demonstrate that integrating V2H technology with renewable energy systems enhances the overall resiliency and flexibility of decentralized power systems. Moreover, this EV to-home integration reduces reliance on traditional backup energy sources, thereby enhancing sustainability. Thus, this study provides a comprehensive framework for integrating solar PV power and EV technologies into decentralized power systems, demonstrating their potential to create scalable, reliable, and environmentally sustainable solutions for rural and remote electrification needs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".