Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes Article Swipe
Mile Mitrovic
,
Aleksandr Lukashevich
,
Petr Vorobev
,
Vladimir Terzija
,
Yury Maximov
,
Deepjyoti Deka
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.14814
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.14814
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.14814
- https://arxiv.org/pdf/2208.14814
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294321157
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4294321157Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2208.14814Digital Object Identifier
- Title
-
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-30Full publication date if available
- Authors
-
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti DekaList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.14814Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2208.14814Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2208.14814Direct OA link when available
- Concepts
-
Power flow, Computer science, Gaussian, Mathematical optimization, Nonlinear system, Electricity, Flow (mathematics), AC power, Power (physics), Electric power system, Renewable energy, Electricity generation, Mathematics, Engineering, Quantum mechanics, Physics, Geometry, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.practical | 54 |
| abstract_inverted_index.processes | 77 |
| abstract_inverted_index.solutions | 115 |
| abstract_inverted_index.demanding, | 50 |
| abstract_inverted_index.efficiency | 13, 92 |
| abstract_inverted_index.generation | 16, 20 |
| abstract_inverted_index.trade-off, | 62 |
| abstract_inverted_index.alternating | 1 |
| abstract_inverted_index.data-driven | 68 |
| abstract_inverted_index.electricity | 15 |
| abstract_inverted_index.renewables. | 36 |
| abstract_inverted_index.uncertainty. | 21, 88 |
| abstract_inverted_index.computationally | 49 |
| abstract_inverted_index.state-of-the-art | 119 |
| abstract_inverted_index.chance-constrained | 4 |
| abstract_inverted_index.complexity/accuracy | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |