Abstract: Aiming at the problem of large dynamic scheduling of units in existing scheduling methods, this paper studies the power system load scheduling method of chi square dimension reduction and Logistic under the condition of high quality and good price of electricity. Using chi square test to reduce the dimensionality of load feature vectors, establish a prediction model to forecast the impact of business expansion capacity on load. Using Logistic model to fit and predict the expansion load components of various industries, and overlaying them to obtain the predicted value of user load. Construct an optimal scheduling model, perform basic linear programming, and introduce integer constraint optimization. Establish a collaborative scheduling model to optimize scheduling prediction data, dynamically adjust variables in the computer group, achieve dynamic partition optimization scheduling, and ensure stable operation of the power system. The experimental results show that the predicted curve of the model is highly consistent with the actual load curve, and the dynamic scheduling of the unit is basically maintained at 0 MW, indicating that the scheduling effect of this method is better.