Ant colony optimization for reactive power dispatch and control strategy design of grid-connected converters under multi-constrained active loss minimization

A mathematical model for reactive power optimization is established based on nodal power-flow equations, incorporating voltage, stability, and equipment constraints, with an adaptive penalty mechanism ensuring feasibility. The ACO algorithm is modified by heuristic-biased initialization and dynamic pheromone updating to improve convergence and robustness. Experimental validation covers IEEE−14 and IEEE−30 bus systems, a microgrid capacity planning case, and hardware-in-the-loop (HIL) tests. Reactive power optimization of grid-connected converters remains an important measure for reducing network losses and ensuring overall stable system operation. In this study, an ant colony optimization (ACO) algorithm is applied to solve the stability-aware reactive power dispatch problem on the IEEE−14 bus test system. The main problem addressed is how to reduce active network loss while keeping bus voltage, reactive power output, and converter operating constraints within feasible ranges. The proposed framework combines power-flow-based fitness evaluation, adaptive penalty handling, and heuristic-guided ant search to improve feasibility and convergence. Each experiment is repeated 100 times with different random seeds to ensure statistical robustness, and comparative baselines include Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results show that ACO achieves an optimized average active network loss of 0.1345 p.u. (mean ± SD: 0.1345 ± 0.0002), consistent with IEEE−14 benchmark values, corresponding to a network loss range of 0.134–0.135 p.u. Compared with GA and PSO, ACO reduces average system loss by 12.5–9.7%, respectively, and shortens convergence iterations by 50%. In addition, in the microgrid capacity optimization case, the proposed method achieves an annual economic cost reduction of 38.76% compared with GA. These results show that the proposed ACO framework achieves reliable optimization performance and introduces three novelties: (i) stability-aware constraints in reactive power optimization, (ii) adaptive penalty scheduling for convergence, and (iii) heuristic initialization for converter dispatch, ensuring broader applicability in control and planning.