Research on Reactive Power Optimization of Distribution Network with SVG and DG Based on Improved Quantum Particle Swarm Algorithm

To effectively address the issues of voltage fluctuation, harmonic pollution and three-phase imbalance caused by the large-scale integration of distributed generation into distribution networks under the background of energy structure optimization, this paper proposes a control strategy that integrates the coordinated operation of distributed generation and static var generators. Given the discrete and nonlinear characteristics of reactive power optimization in distribution networks, an improved quantum particle swarm optimization algorithm is adopted for solution. This algorithm significantly enhances the global search ability of particles by introducing an adaptively adjusted inertia weight; it greatly expands the coverage of the population by initializing population information with the Logistic chaotic mapping; and it effectively increases the diversity of the population and expands the search space of the algorithm by combining the Levy flight strategy and the Cauchy mutation strategy. Finally, the IEEE-33 node system is taken as a case, and simulation analysis is conducted by integrating distributed generation and static var generators at different nodes. The results show that the improved quantum particle swarm optimization algorithm demonstrates a faster convergence speed and higher solution accuracy in reactive power optimization of distribution networks with distributed generation and static var generators, significantly reducing the total network loss of the system and greatly improving the stability of node voltages.