New energy integration and flexible demand response make smart grid operation scenarios complex and changeable

which bring challenges to network planning. If every possible scenario is considered

the solution to the planning can become extremely time-consuming and difficult. This paper introduces statistical machine learning (SML) techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks. The proposed SML includes linear regression

probability distribution

Markov chain

isoprobabilistic transformation

maximum likelihood estimator

stochastic response surface and center point method. Based on the above SML model

capricious weather

photovoltaic power generation

thermal load

power flow and uncertainty programming are simulated. Taking a 33-bus distribution system as an example

this paper compares the stochastic planning model based on SML with the traditional models published in the literature. The results verify that the proposed model greatly improves planning performance while meeting accuracy requirements. The case study also considers a realistic power distribution system operating under stressed conditions.