Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity Article Swipe
Yan Shi
,
Maoyi Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2404/1/012015
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2404/1/012015
With the wide application of distributed generation technology, in order to maintain the stable and safe operation of the distribution network, this paper considers the uncertainty of power generation and a load of distributed energy storage. It takes the distribution network with distributed energy storage as the research object, models and analyzes the optimization problem, and studies the problem of DG configuration by using the reinforcement learning method. In the case of distributed grid connection, the voltage distribution change of the distribution network and the impact of network loss are analyzed. The line power flow of the distribution network is calculated by using the MATLAB platform as a platform, and the power flow calculation of the distribution network is simulated and analyzed by the MATLAB platform. The system loss is significantly reduced. The experimental results show that the system loss variance is 29.19. After adding energy storage, the loss variance is 14.77, which is reduced by 49.41%. According to the scheme, the voltage stability and operating loss of the system can be effectively improved.
Related Topics
Concepts
Distributed generation
MATLAB
Sensitivity (control systems)
Energy storage
Computer science
Power (physics)
Stability (learning theory)
Voltage
Variance (accounting)
Control theory (sociology)
Electronic engineering
Renewable energy
Engineering
Electrical engineering
Machine learning
Quantum mechanics
Artificial intelligence
Physics
Control (management)
Accounting
Business
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2404/1/012015
- https://iopscience.iop.org/article/10.1088/1742-6596/2404/1/012015/pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311606168
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311606168Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2404/1/012015Digital Object Identifier
- Title
-
Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss SensitivityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-01Full publication date if available
- Authors
-
Yan Shi, Maoyi HuangList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2404/1/012015Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2404/1/012015/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1742-6596/2404/1/012015/pdfDirect OA link when available
- Concepts
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Distributed generation, MATLAB, Sensitivity (control systems), Energy storage, Computer science, Power (physics), Stability (learning theory), Voltage, Variance (accounting), Control theory (sociology), Electronic engineering, Renewable energy, Engineering, Electrical engineering, Machine learning, Quantum mechanics, Artificial intelligence, Physics, Control (management), Accounting, Business, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
9Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.adding | 145 |
| abstract_inverted_index.change | 79 |
| abstract_inverted_index.energy | 35, 44, 146 |
| abstract_inverted_index.impact | 86 |
| abstract_inverted_index.models | 50 |
| abstract_inverted_index.stable | 14 |
| abstract_inverted_index.system | 128, 139, 170 |
| abstract_inverted_index.49.41%. | 157 |
| abstract_inverted_index.method. | 68 |
| abstract_inverted_index.network | 41, 83, 88, 99, 118 |
| abstract_inverted_index.object, | 49 |
| abstract_inverted_index.problem | 59 |
| abstract_inverted_index.reduced | 155 |
| abstract_inverted_index.results | 135 |
| abstract_inverted_index.scheme, | 161 |
| abstract_inverted_index.storage | 45 |
| abstract_inverted_index.studies | 57 |
| abstract_inverted_index.voltage | 77, 163 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analyzed | 122 |
| abstract_inverted_index.analyzes | 52 |
| abstract_inverted_index.learning | 67 |
| abstract_inverted_index.maintain | 12 |
| abstract_inverted_index.network, | 21 |
| abstract_inverted_index.platform | 106 |
| abstract_inverted_index.problem, | 55 |
| abstract_inverted_index.reduced. | 132 |
| abstract_inverted_index.research | 48 |
| abstract_inverted_index.storage, | 147 |
| abstract_inverted_index.storage. | 36 |
| abstract_inverted_index.variance | 141, 150 |
| abstract_inverted_index.According | 158 |
| abstract_inverted_index.analyzed. | 91 |
| abstract_inverted_index.considers | 24 |
| abstract_inverted_index.improved. | 174 |
| abstract_inverted_index.operating | 166 |
| abstract_inverted_index.operation | 17 |
| abstract_inverted_index.platform, | 109 |
| abstract_inverted_index.platform. | 126 |
| abstract_inverted_index.simulated | 120 |
| abstract_inverted_index.stability | 164 |
| abstract_inverted_index.calculated | 101 |
| abstract_inverted_index.generation | 7, 29 |
| abstract_inverted_index.application | 4 |
| abstract_inverted_index.calculation | 114 |
| abstract_inverted_index.connection, | 75 |
| abstract_inverted_index.distributed | 6, 34, 43, 73 |
| abstract_inverted_index.effectively | 173 |
| abstract_inverted_index.technology, | 8 |
| abstract_inverted_index.uncertainty | 26 |
| abstract_inverted_index.distribution | 20, 40, 78, 82, 98, 117 |
| abstract_inverted_index.experimental | 134 |
| abstract_inverted_index.optimization | 54 |
| abstract_inverted_index.configuration | 62 |
| abstract_inverted_index.reinforcement | 66 |
| abstract_inverted_index.significantly | 131 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5054991549 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.56592962 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |