TY  - EJOU
AU  - Ruan, Xuan 
AU  - Yan, Han 
AU  - Hu, Donglin 
AU  - Zhang, Min 
AU  - Li, Ying 
AU  - Hai, Di 
AU  - Yang, Bo 

TI  - Improved Gain Shared Knowledge Optimizer Based Reactive Power Optimization for Various Renewable Penetrated Power Grids with Static Var Generator Participation
T2  - Energy Engineering

PY  - 2026
VL  - 123
IS  - 3
SN  - 1546-0118

AB  - An optimized volt-ampere reactive (VAR) control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators (SVGs). The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions, and quantifies SVG high-speed compensation capability, enabling seamless transition from localized VAR management to a globally coordinated strategy. An enhanced adaptive gain-sharing knowledge optimizer (AGSK-SD) integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions, renewable source reactive-power set-points, and SVG output. The algorithm adaptively modulates knowledge factors and ratios across search phases, performs SA-based fine-grained local exploitation, and periodically re-injects population diversity to prevent premature convergence. Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume (HV), inverse generative distance (IGD), and spread metrics while maintaining acceptable computational burden. The method reduces network losses from 2.7191 to 2.15 MW (20.79% reduction) and from 15.1891 to 11.22 MW (26.16% reduction) in the 9-bus and 39-bus systems respectively. Simultaneously, the cumulative voltage-deviation index decreases from 0.0277 to 3.42 × 10<sup>−4</sup> p.u. (98.77% reduction) in the 9-bus system, and from 0.0556 to 0.0107 p.u. (80.76% reduction) in the 39-bus system. These improvements demonstrate significant suppression of line losses and voltage fluctuations. Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
KW  - Gained-sharing knowledge improved algorithm; adaptive parameter adjustment; simulated annealing local search algorithms; diversity enhancement mechanisms; wind and solar new energy; static var generator; reactive power optimization

DO  - 10.32604/ee.2025.071166