Coordinated load management of building clusters and electric vehicles charging: An economic model predictive control investigation in demand response
Bibliographic record
Abstract
This paper introduces a probabilistic load coordination approach to optimize the combined management of home-charged electric vehicles and space heating demand. Reduced-order resistance–capacitance models are applied for building thermal simulations, while support vector machine models predict baseline electric loads. Monte Carlo simulations are used to estimate arrival times and remaining charge of electric vehicles, assessing the advantages of Level 1 and Level 2 infrastructure for one-way and two-way residential charging stations. The Individual Stress Level, a novel metric for supervisor coordination within an economic model predictive control framework, is introduced. The methodology is tested on ten homes managed by an energy aggregator in Québec, Canada. Results show monodirectional electric vehicle charging not disrupting grid stability under static-price tariffs. However, time-of-use pricing structures increase average demand by 19–29% and peak capacity by 3–18%. Bidirectional scenarios indicate a 15.5% increase in maximum demand and a 17.5% rise in normal capacity. The combined building energy flexibility index indicates reductions of 74–140% for morning events and 54–98% for evening events. A sensitivity analysis highlights the role of demand charges in the Individual Stress Level activation, showing reduced price sensitivity for monodirectional setups compared to bidirectional configurations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".