Work

A Two-Stage Intelligent Reactive Power Optimization Method for Power Grids Based on Dynamic Voltage Partitioning

Year: 2026

Type: article

Abstract: Aiming at issues such as reactive power distribution fluctuations and insufficient local support caused by large-scale integration of renewable energy in new power systems, as well as the poor adaptability of traditional methods and bottlenecks of deep reinforcement learning in complex power grids, a two-stage intelligent optimization method for grid reactive power based on dynamic voltage partitioning is proposed. Firstly, a comprehensive indicator system covering modularity, regulation capability, and membership degree is constructed. Adaptive MOPSO is employed to optimize K-means clustering centers, achieving dynamic grid partitioning and decoupling large-scale optimization problems. Secondly, a Markov Decision Process model is established for each partition, incorporating a penalty mechanism for safety constraint violations into the reward function. The DDPG algorithm is improved through multi-experience pool probabilistic replay and sampling mechanisms to enhance agent training. Finally, an optimal reactive power regulation scheme is obtained through two-stage collaborative optimization. Simulation case studies demonstrate that this method effectively reduces solution complexity, accelerates convergence, accurately addresses reactive power dynamic distribution and local support deficiencies, and ensures voltage security and optimal grid losses.

Source: Electronics

DOI: 10.3390/electronics15020447

Authors: Tianliang Xue, Xianxin Gan, Lei Zhang, Su Wang, Qin Li, Qiuting Guo

Institutions: China Three Gorges University, Tsinghua University

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896