Day-Ahead Coordinated Reactive Power Optimization Dispatching Based on Semidefinite Programming

Binbin Xu, Mengqi Liu, Yilin Zhong, Peijie Cong, Bo Zhu, Tao Liu, Yujun Li () and Zhengchun Du
Additional contact information
Binbin Xu: Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510699, China
Mengqi Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yilin Zhong: China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Peijie Cong: Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510699, China
Bo Zhu: Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510699, China
Tao Liu: China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Yujun Li: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Zhengchun Du: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2025, vol. 18, issue 20, 1-21

Abstract: With access to new energy sources, the problem of reactive power optimization and dispatching has become increasingly important for research. However, the reactive power optimization problem is a mixed integer nonlinear optimization problem. In order to solve the integer variables and nonlinear conditions existing therein, a method for coordinated reactive power optimization and dispatching based on semidefinite programming is proposed. Firstly, a reactive power optimization model considering discrete variables and continuous variables is established with the minimization of total operating cost as the objective function; secondly, the discrete variables are transformed into equality constraints by quadratic equations, and then a solvable semi-definite programming problem is obtained; thirdly, the rank-one constraint is restored by the Iterative Optimization based Gaussian Randomization Method (IOGRM), and the optimal solution equivalent to the original problem is obtained. Finally, the correctness and effectiveness of the proposed model and solution method are verified by analyzing and comparing with the second-order cone programming (SOCP) through the modified IEEE standard example.

Keywords: reactive power optimization; gaussian randomization; iterative method; semidefinite programming (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: 2025
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