A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability

Xiping Ma (), Wenxi Zhen, Rui Xu, Xiaoyang Dong and Yaxin Li
Additional contact information
Xiping Ma: Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Wenxi Zhen: Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Rui Xu: Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Xiaoyang Dong: Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Yaxin Li: Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China

Energies, 2024, vol. 17, issue 16, 1-18

Abstract: With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results.

Keywords: reactive power optimization; bi-level structure; reactive power capability analysis; improved artificial fish schooling algorithm (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3910/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3910/ (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:17:y:2024:i:16:p:3910-:d:1452063

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 ().