  A surrogate model that accurately predicts distribution system voltages is
crucial for reliable smart grid planning and operation. This letter proposes a
fixed-point data-driven surrogate modeling method that employs a limited
dataset to learn the power-voltage relationship of an unbalanced three-phase
distribution system. The proposed surrogate model is designed using a
fixed-point load-flow equation, and the stochastic gradient descent method with
an automatic differentiation technique is employed to update the parameters of
the surrogate model using complex power and voltage samples. Numerical examples
in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed
surrogate model can outperform surrogate models based on the deep neural
network and Gaussian process regarding prediction accuracy and sample
efficiency
