Uncertainty Quantification and Control in Power System Security and Operation Via Data-Driven Polynomial Chaos Expansion Based Methods
Bibliographic record
Abstract
The global energy situation is shifting towards renewable energy sources (RESs) to promote sustainability and reduce fossil fuel reliance.This shift brings uncertainties from volatile RESs and new forms of loads (e.g., electric vehicles), challenging power system operation and security.Addressing these challenges, this thesis aims to leverage a surrogate modeling method, namely the polynomial chaos expansion method, to systematically investigate and mitigate the impacts of uncertainties on power system transfer capability and economic dispatch (ED).The overarching goal is to offer vital guidance for ensuring and enhancing the security of power systems while maximizing the utilization of transmission assets and economic benefits, considering the high uncertainty level of current and future power grids.The thesis first studies the impacts of uncertainties brought by volatile RESs, random loads, and unforeseen equipment outages on power system available transfer capability (ATC), a crucial index in power system security analysis.By exploiting polynomial chaos theory and moment-based methods, a data-driven sparse polynomial chaos expansion (DDSPCE) method is developed for probabilistic total transfer capability (PTTC) and ATC assessment.Notably, without requiring preassumed probability distributions of random inputs, the proposed DDSPCE directly exploits data for estimating the probabilistic characteristics of PTTC (e.g., mean, variance, probability density function (PDF), and cumulative distribution function (CDF)), based on which the ATC with a certain confidence level can be readily calculated.An integrated sparse framework further enhances its computational efficiency and accuracy.Simulations on the modified IEEE 118-bus system and the modified PEGASE 1354-bus system validate the DDSPCE method's efficacy in PTTC evaluation.Furthermore, the results underscore the significance of incorporating discrete uncertainties, like equipment outages, in both PTTC and ATC assessments.The thesis then delves into the impacts of uncertainties, especially from wind power, on ED, a critical aspect of the power system daily operation.A DDSPCE-based surrogate modeling method is developed to estimate the probabilistic characteristics of ED solutions, including their mean, variance, and distribution functions.The developed method can handle extensive random inputs without their predefined probability distributions.Extensive simulation results on an integrated electricity and gas system (IEGS) using real-life wind power data validate the efficiency and effectiveness of the proposed method in quantifying the impacts of uncertainties on the ED solutions, even when the ED solutions are multimodal.These results highlight the DDSPCE method's efficacy and efficiency in addressing general and complex scenarios.viii my Ph.D. studies has been invaluable.Most of all, my Ph.D. studies would not have been completed without the support of my family.I would like to
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".