Ant Colony Optimization Cuts Grid Converter Losses and Halves Iterations

An engineering investigation evaluating stability-aware reactive power dispatch has shown that a modified ant colony optimization framework substantially lowers active network losses while halving the computational iterations needed to reach convergence. Testing conducted on the standard IEEE-14 bus test system established that the ant colony optimization algorithm achieved an optimized average active network loss of 0.1345 per unit, with recorded values spanning a narrow range between 0.134 and 0.135 per unit. Across benchmark evaluations, the proposed method decreased average active system losses by 12.5 percent compared with a Genetic Algorithm baseline and by 9.7 percent compared with Particle Swarm Optimization. In addition to reducing electrical transmission losses, the ant colony optimization approach shortened convergence iterations by 50 percent relative to both alternative algorithms, demonstrating simultaneous improvements in dispatch quality and iterative solution speed for grid-connected converters.

The research addresses the central technical challenge of minimizing active network losses while rigorously maintaining bus voltages, converter reactive power outputs, and operating constraints within allowable limits. Reactive power optimization of grid-connected converters serves as an important operational measure for curtailing active power dissipation and supporting the overall stability of modern power systems.

To establish a rigorous mathematical foundation for reactive power optimization, the researchers formulated an analytical model based on nodal power-flow equations. This governing framework incorporates nodal voltage limits, stability conditions, and converter equipment constraints directly into the optimization architecture.

The resulting framework combines power-flow-based fitness evaluation, adaptive penalty handling, and heuristic-guided ant searching to enhance both search feasibility and algorithmic convergence.

The experimental validation program encompassed testing across standard IEEE-14 and IEEE-30 bus systems, a dedicated microgrid capacity planning case, and hardware-in-the-loop tests. To ensure statistical robustness, each experiment was repeated 100 times utilizing different random seeds. On the IEEE-14 benchmark system, the ant colony optimization algorithm achieved an average active loss of 0.1345 per unit with a standard deviation of 0.0002 per unit, confirming high numerical consistency with established benchmark standards.

In the microgrid capacity optimization case, the proposed ant colony optimization method achieved an annual economic cost reduction of 38.76 percent compared with the Genetic Algorithm baseline, illustrating its utility in economic resource sizing. Based on these findings, the authors reported that the proposed framework delivers reliable optimization performance across power engineering applications. The study highlights three primary novelties: the integration of stability-aware constraints directly into reactive power optimization, the implementation of adaptive penalty scheduling to secure convergence, and the development of heuristic initialization tailored for converter dispatch, ensuring broad practical applicability in converter control strategy design and long-term planning.

Subject of Research: Reactive power dispatch and active loss minimization for grid-connected converters

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

Article References: Wang, Q., Yang, J., Cao, T., Zhu, H., & Wang, P. (2026). Ant colony optimization for reactive power dispatch and control strategy design of grid-connected converters under multi-constrained active loss minimization. Discover Applied Sciences. https://doi.org/10.1007/s42452-026-09614-w

Image Credits: AI Generated

DOI: 10.1007/s42452-026-09614-w

Keywords: ant colony optimization, reactive power dispatch, grid-connected converters, active loss minimization, power flow equations, IEEE-14 bus system, microgrid capacity planning, adaptive penalty mechanism, colony, optimization, reactive, power