Uncertainty Impact on Aggregator Performance for Peak Shaving
Bibliographic record
Abstract
Accurate peak period forecasting and load consumption profile prediction are critical for utility resource planning and emergency response preparedness. This article investigates the impact of uncertainties in both peak period characteristics (timing and magnitude) and thermostatically controlled loads (TCLs) behavior on capacity estimation and subsequent peak shaving performance. Uncertainty sources associated with common TCLs and challenges with forecasting peak period parameters are discussed. Conventional TCL models are updated to address and incorporate these inaccuracies and uncertainty factors. We design and implement a management system that aggregates TCLs using a comprehensive virtual battery (VB) model, ensuring adherence to all operational constraints. An optimal peak shaving algorithm is deployed during peak demand periods utilizing this VB framework, enabling comparative analysis of “OFF” and “ON/OFF” control scenarios. A novel metric, the shared load capacity percentage, is introduced to provide insights for peak shaving optimization and broader demand response (DR) applications. Findings indicate that employing an ON/OFF control strategy can potentially result in 20% more capacity compared to OFF control, particularly when baseline energy consumption is reduced by 10%. Furthermore, we present a detailed assessment of the impact of various uncertainties on the peak shaving process, considering both utility operations and end-user loads. The sensitivity of the proposed control system to forecasting inaccuracies is thoroughly analyzed, and the expected loss of loads’ hybrid reserved energy is quantified. A metric for aggregator reserved capacity loss is introduced, giving insight to the detrimental effects of forecasting errors on peak shaving efficacy, encompassing performance, cost, and temporal implications. The results reveal that inaccurate peak time forecasting and voltage level uncertainties can lead to up to a 50% variation of peak shaving capacity with electric water heaters (EWHs) and heat pumps (HPs). The study also demonstrates that ambient temperature forecasting inaccuracies lead to a 35% capacity provision change. These findings underscore the critical need for robust forecasting techniques and uncertainty management strategies to implement TCL-based peak shaving programs effectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".