Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization

Minsheng Yang, Jianqi Li, Rui Du, Jianying Li, Jian Sun, Xiaofang Yuan, Jiazhu Xu and Shifu Huang
Additional contact information
Minsheng Yang: College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Jianqi Li: College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Rui Du: College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Jianying Li: College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Jian Sun: College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
Xiaofang Yuan: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Jiazhu Xu: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Shifu Huang: Changde Guoli Transformer Co., Ltd., Changde 415000, China

Energies, 2022, vol. 15, issue 6, 1-16

Abstract: Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.

Keywords: interval optimization; second-order cone relaxation; confidence interval estimation; optimal power flow; reactive power optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/6/2235/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/6/2235/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:6:p:2235-:d:774452

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().