This invention discloses a distributed AC optimal
power flow solution method based on a multi-
branch neural network, comprising: establishing an AC optimal
power flow model; modeling the power
system as a weighted
graph based on
complex network theory, and using the Louvain
algorithm to perform
community partitioning on this weighted graph; training a multi-
branch DNN model for the entire power
system, and using this model to learn the mapping relationship between input features and output features; using the trained DNN model to provide predicted voltages of non-zero injected nodes in the region using input features; calculating the voltages of ZigBs (Zones of Injection) using the Kron simplification method; obtaining predicted active and
reactive load values based on the voltages of the ZigBs and the provided load, and obtaining the
demand load through the predicted load values. This invention can effectively reduce the
network size, improve training efficiency, and enhance the accuracy of the solution. Furthermore, its flexibility and robustness make it more capable of handling future complex power
system optimization tasks.