Clustering-driven design and predictive control of hybrid PV-battery storage systems for demand response in energy communities
Bibliographic record
Abstract
The integration of renewable energy sources and participation in demand response requires advanced modelling and control strategies to enhance building-grid interaction. This study presents a comprehensive methodology for selecting typical days and evaluating how controllable building thermal loads influence the design and operation of grid-supportive technologies, specifically photovoltaic (PV) and battery storage systems. Typical days are identified through dynamic time warping (DTW) and hierarchical clustering approaches, supported by six internal validation metrics. Grey-box and regression models are employed to predict building energy consumption, while PV and battery models assess system performance. A two-level Model Predictive Control (MPC) framework is employed to optimize the buildings demand and coordinate the operation of grid-supportive technologies. At the first level, a distributed MPC algorithm manages thermal loads in individual buildings to enable demand response. At the second level, a supervisory MPC optimizes the operation of the hybrid PV-battery storage system to achieve targeted grid flexibility. The case study considers a virtual community in Varennes, Québec, consisting of institutional and residential buildings. Through efficient thermal load management, the methodology shows that community peak demand can be reduced by over 40% compared to current operational practices, and the required capacity of grid-supportive systems can be reduced by up to 26% in a worst-case scenario analysis.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".