An adaptive multi-objective reactive power optimization control strategy for wind-integrated power systems based on swarming genetic algorithm

Authors

• Haoyu Liu, School of Electrical & Information Engineering ， Changsha University of Science & Technology ， Changsha 410114， China;State Key Laboratory of Disaster Prevention & Reduction for Power Grid ， Changsha University of Science & Technology ， Changsha 410114， China
• Yuan Zhao, School of Electrical & Information Engineering ， Changsha University of Science & Technology ， Changsha 410114， China;State Key Laboratory of Disaster Prevention & Reduction for Power Grid ， Changsha University of Science & Technology ， Changsha 410114， China
• Pengfei Dai, School of Electrical & Information Engineering ， Changsha University of Science & Technology ， Changsha 410114， China;State Key Laboratory of Disaster Prevention & Reduction for Power Grid ， Changsha University of Science & Technology ， Changsha 410114， China
• Xin Tang, School of Electrical & Information Engineering ， Changsha University of Science & Technology ， Changsha 410114， China;State Key Laboratory of Disaster Prevention & Reduction for Power Grid ， Changsha University of Science & Technology ， Changsha 410114， ChinaFollow
• Chongru Liu, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources ， North China Electric Power University ， Beijing 102206， China

Keywords

reactive power optimization, wind power integration, scenario partitioning, swarming genetic algorithm, dynamic weighting mechanism, multi-scenario collaborative optimization

Abstract

An adaptive multi-objective reactive power optimization control strategy considering the scenario partitioning of wind turbines is proposed to address the issues of voltage fluctuation and reactive power imbalance in new power systems caused by the strong randomness and intermittency of wind power. A multi-scenario collaborative optimization model is constructed using the Weibull wind speed probability distribution. A comprehensive reactive power index based on scenario partitioning is proposed to quantify the impact of wind power output uncertainty, and a dynamic weighting mechanism is designed to adaptively balance the objectives of voltage security and network loss economy. A swarming genetic algorithm, which integrates the global search mechanism of genetic algorithms and the fast convergence characteristics of particle swarm optimization, is developed to synchronously coordinate the reactive power output of wind turbines, the switching of discrete capacitor banks, and the continuous regulation of SVG, achieving dynamic optimization across multiple time scales. Simulation results demonstrate that the collaborative optimization of power system security and economy under multi-scenario wind power grid connection is achieved by the proposed algorithm, and its comprehensive regulation advantages in complex power systems are validated.

DOI

10.19781/j.issn.1673-9140.2026.03.004

First Page

35

Last Page

45

Recommended Citation

Liu, Haoyu; Zhao, Yuan; Dai, Pengfei; Tang, Xin; and Liu, Chongru (2026) "An adaptive multi-objective reactive power optimization control strategy for wind-integrated power systems based on swarming genetic algorithm," Journal of Electric Power Science and Technology: Vol. 41: Iss. 3, Article 4.
DOI: 10.19781/j.issn.1673-9140.2026.03.004
Available at: https://jepst.researchcommons.org/journal/vol41/iss3/4