Open Access

ARTICLE

Reactive Power Optimization Strategy for Distribution Networks Based on Analytical Transformation of Probabilistic Power Flow Sensitivity

Haiqing Cai^1,2, Liang Tu^1,2, Wei Chen^3,4, Wencong Wu^3,4, Qingyan Zhang⁵, Jian Wang^5,*
1 State Key Laboratory of HVDC, Electric Power Research Institute, China Southern Power Grid, Guangzhou, China
2 National Energy Power Grid Technology R&D Centre, Guangzhou, China
3 Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System, Guangzhou, China
4 CSG Key Laboratory for Power System Simulation, Electric Power Research Institute, China Southern Power Grid, Guangzhou, China
5 School of Electrical and Power Engineering, Hohai University, Nanjing, China
* Corresponding Author: Jian Wang. Email:
(This article belongs to the Special Issue: Operation and Control of Grid-connected New Energy and Emerging Loads)

Energy Engineering https://doi.org/10.32604/ee.2026.080048

Received 02 February 2026; Accepted 27 February 2026; Published online 23 March 2026

Abstract

To address the difficulty of adapting reactive power optimization strategies in distribution networks to diverse scenarios due to source-load uncertainty, which increases the risk of over-voltage and overloads, a reactive power optimization strategy for distribution networks is proposed based on probabilistic power flow sensitivity. Firstly, considering the impact of source-load uncertainty on the dispatching strategy of distribution networks, a reactive power optimization model based on chance constraints is constructed, and the probabilistic models of random variables for voltage and branch power fluctuations in the chance constraints are respectively characterized by probabilistic power flow sensitivity. Then, the quadratic nonlinear branch safety constraints are linearized by the polygonal approximation method to decouple the state variables and random variables. Through the affine transformation of probabilistic power flow sensitivity, the probabilistic analytical expressions of voltage and branch safety chance constraints are constructed. Finally, based on the probability transformation theory, the nonlinear chance constraints in probability form are analytically transformed, and the reactive power optimization of distribution networks is converted into a solvable mixed-integer second-order cone programming problem. Through simulation verification, the proposed method in this paper can fully exploit the reactive power regulation capacity of photovoltaic power, and has obvious advantages in voltage optimization effect and computational efficiency compared with the conventional sample mean method.

---

Keywords

Distribution network; source-load uncertainty; reactive power optimization; chance constraint; probabilistic power flow sensitivity

---