Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning Article Swipe
Mohammad Rajabdorri
,
Behzad Kazemtabrizi
,
Matthias C. M. Troffaes
,
Lukas Sigrist
,
Enrique Lobato
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.01540
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.01540
As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.
Related Topics
Concepts
Nadir
Computer science
Constraint (computer-aided design)
Power system simulation
Artificial intelligence
Electric power system
Support vector machine
Control theory (sociology)
Mathematical optimization
Power (physics)
Machine learning
Mathematics
Engineering
Control (management)
Satellite
Geometry
Physics
Aerospace engineering
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.01540
- https://arxiv.org/pdf/2210.01540
- OA Status
- green
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4302306382
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4302306382Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.01540Digital Object Identifier
- Title
-
Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-04Full publication date if available
- Authors
-
Mohammad Rajabdorri, Behzad Kazemtabrizi, Matthias C. M. Troffaes, Lukas Sigrist, Enrique LobatoList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.01540Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2210.01540Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2210.01540Direct OA link when available
- Concepts
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Nadir, Computer science, Constraint (computer-aided design), Power system simulation, Artificial intelligence, Electric power system, Support vector machine, Control theory (sociology), Mathematical optimization, Power (physics), Machine learning, Mathematics, Engineering, Control (management), Satellite, Geometry, Physics, Aerospace engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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12Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.reduce | 5 |
| abstract_inverted_index.solved | 172 |
| abstract_inverted_index.system | 132 |
| abstract_inverted_index.vector | 103 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.carried | 138 |
| abstract_inverted_index.classic | 94 |
| abstract_inverted_index.compare | 116 |
| abstract_inverted_index.dataset | 86 |
| abstract_inverted_index.energy, | 18 |
| abstract_inverted_index.machine | 80, 95, 118, 165 |
| abstract_inverted_index.methods | 120, 143 |
| abstract_inverted_index.outages | 29 |
| abstract_inverted_index.predict | 108 |
| abstract_inverted_index.problem | 162 |
| abstract_inverted_index.quality | 187 |
| abstract_inverted_index.reduced | 32 |
| abstract_inverted_index.results | 156 |
| abstract_inverted_index.support | 102 |
| abstract_inverted_index.systems | 20 |
| abstract_inverted_index.thermal | 9 |
| abstract_inverted_index.becoming | 23 |
| abstract_inverted_index.included | 44 |
| abstract_inverted_index.increase | 13 |
| abstract_inverted_index.inertia. | 35 |
| abstract_inverted_index.learning | 96, 119, 166 |
| abstract_inverted_index.logistic | 99 |
| abstract_inverted_index.machine, | 104 |
| abstract_inverted_index.methods, | 97 |
| abstract_inverted_index.outages. | 189 |
| abstract_inverted_index.problem, | 78 |
| abstract_inverted_index.process, | 48 |
| abstract_inverted_index.proposed | 66, 106, 142 |
| abstract_inverted_index.response | 186 |
| abstract_inverted_index.training | 85 |
| abstract_inverted_index.achieving | 182 |
| abstract_inverted_index.available | 93 |
| abstract_inverted_index.deviation | 54 |
| abstract_inverted_index.frequency | 26, 40, 53, 71, 110, 123, 153, 168, 185 |
| abstract_inverted_index.integrate | 68 |
| abstract_inverted_index.intention | 2 |
| abstract_inverted_index.learning. | 81 |
| abstract_inverted_index.synthetic | 84 |
| abstract_inverted_index.tolerable | 52 |
| abstract_inverted_index.acceptable | 184 |
| abstract_inverted_index.analytical | 148, 178 |
| abstract_inverted_index.commitment | 77, 126, 161 |
| abstract_inverted_index.constraint | 73, 170 |
| abstract_inverted_index.generated. | 88 |
| abstract_inverted_index.generation | 10 |
| abstract_inverted_index.linearized | 149 |
| abstract_inverted_index.non-linear | 70 |
| abstract_inverted_index.regression | 100 |
| abstract_inverted_index.scheduling | 47 |
| abstract_inverted_index.approaches, | 127 |
| abstract_inverted_index.constrained | 124 |
| abstract_inverted_index.constraints | 41 |
| abstract_inverted_index.formulation | 150 |
| abstract_inverted_index.instability | 27 |
| abstract_inverted_index.simulations | 128 |
| abstract_inverted_index.susceptible | 24 |
| abstract_inverted_index.traditional | 122 |
| abstract_inverted_index.considerably | 173 |
| abstract_inverted_index.formulation, | 179 |
| abstract_inverted_index.increasingly | 22 |
| abstract_inverted_index.contingencies. | 59 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.10109146 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |