A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation
The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which ca...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/6678942 |
| _version_ | 1797647610725531648 |
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| author | Honglei Jia Cong Zhang Jieming Du Na Kuang |
| author_facet | Honglei Jia Cong Zhang Jieming Du Na Kuang |
| author_sort | Honglei Jia |
| collection | DOAJ |
| description | The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. |
| first_indexed | 2024-03-11T15:19:58Z |
| format | Article |
| id | doaj.art-3c8a76de2327486283a5c7b891c2aa5f |
| institution | Directory Open Access Journal |
| issn | 2050-7038 |
| language | English |
| last_indexed | 2024-03-11T15:19:58Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj.art-3c8a76de2327486283a5c7b891c2aa5f2023-10-29T00:00:00ZengHindawi-WileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/6678942A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power GenerationHonglei Jia0Cong Zhang1Jieming Du2Na Kuang3College of Electrical and Information EngineeringCollege of Electrical and Information EngineeringCollege of Electrical and Information EngineeringSchool of Educational ScienceThe increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero.http://dx.doi.org/10.1155/2023/6678942 |
| spellingShingle | Honglei Jia Cong Zhang Jieming Du Na Kuang A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation International Transactions on Electrical Energy Systems |
| title | A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation |
| title_full | A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation |
| title_fullStr | A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation |
| title_full_unstemmed | A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation |
| title_short | A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation |
| title_sort | data driven approach for reactive power optimization incorporating interval values for renewable power generation |
| url | http://dx.doi.org/10.1155/2023/6678942 |
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