Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part II: Scalability and Applications Article Swipe
Yingqi Liang
,
Junbo Zhao
,
Dhivya Sampath Kumar
,
Ketian Ye
,
Dipti Srinivasan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tpwrs.2022.3218473
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tpwrs.2022.3218473
This two-part paper proposes a data-driven, robust, and scalable sparse estimation framework of distribution factors (DFs). Part I proposes a novel Adaptive M -Lasso estimator with theoretically guaranteed robustness. For solving this estimator, common algorithms are not scalable to large-scale systems since they often fail to converge under parallelism. This is addressed in Part II, where a novel Online Parallel Stochastic Coordinate Descent algorithm with theoretically guaranteed scalability is proposed. It can simultaneously update massive DF estimates by stochastic parallelism with fast convergence. It also works recursively under a multi-core setting and does not need step-size tuning, which is applicable for cloud-computing-based online parallel estimation. Convergence bounds of this algorithm are theoretically proven, and its speedup and maximum parallel core number are derived. These guide practical computational resource allocation for high efficiency. The benefits of the framework are showcased in real-time market operations under locational marginal pricing (LMP) and financial transmission right (FTR) mechanisms for congestion management. Thanks to its sparsity-promoting effect, robustness, and scalability, it allows for correctly and fast solving LMPs, congestion patterns, and FTR revenues against uncertainties. Test results on IEEE 300- and European 9241-bus systems via Alibaba Cloud validate the high scalability of this algorithm and benefits of the proposed framework to system applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tpwrs.2022.3218473
- OA Status
- green
- Cited By
- 6
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312999621
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312999621Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tpwrs.2022.3218473Digital Object Identifier
- Title
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Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part II: Scalability and ApplicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-04Full publication date if available
- Authors
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Yingqi Liang, Junbo Zhao, Dhivya Sampath Kumar, Ketian Ye, Dipti SrinivasanList of authors in order
- Landing page
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https://doi.org/10.1109/tpwrs.2022.3218473Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://figshare.com/articles/journal_contribution/Robust_data-driven_sparse_estimation_of_distribution_factors_considering_PMU_data_quality_and_renewable_energy_uncertainty_-_Part_II_Scalability_and_applications/23797047Direct OA link when available
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Scalability, Computer science, Robustness (evolution), Speedup, Cloud computing, Estimator, Mathematical optimization, Convergence (economics), Parallel computing, Mathematics, Database, Operating system, Statistics, Chemistry, Gene, Biochemistry, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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