Bi-level coordinated optimization method integrating improved artificial fish swarm algorithm and hardware cost model for distribution network

Traditional power flow optimization fails to account for the coupling between network loss and the cost of converters, and overlooks both transmission loss and distribution equipment loss. This paper proposes a bi-level coordinated optimization framework that integrates an improved artificial fish swarm algorithm (AFSA) and a hardware cost model to resolve this conflict. This framework has developed a two-layer model consisting of an X-Y layer optimal power model and a Z-layer optimal reconstruction model, which explicitly combines hardware costs and inverter losses, effectively resolving the conflict between minimizing control actions and reducing system losses. Furthermore, an enhanced AFSA featuring adaptive recombination behavior significantly improves resource utilization efficiency and reduces computation time. The results verified on the experimental distribution network platform show that, compared with traditional methods, the proposed approach reduces the total economic cost by 7.97 %, enhances the wind power consumption capacity by 12.42 %, and increases the average minimum voltage by 6.81 %, while maintaining a comparable level of transmission loss.