Data-Driven Optimal Power Dispatch for Distributed Energy Resources in Radial Feeder using Multi-Stage Regression

This paper develops a multi-stage regression (MSR) based algorithm for dispatching distributed energy resources (DERs) in a power distribution system. Application of the standard regression algorithm to network power flow problems generally suffer from performance limitations due to collinearity in training data, (i.e. voltages, currents and nodal demands), and non-linearity inherent in power flows and DER dispatches. Here, regression collinearity is addressed with intelligent choice of the input data training set which also considerably reduces the requirements on the volume of training data. Next, a logistic regression based labeling scheme is developed and applied to classify the data into disjoint training sets which significantly improves the prediction accuracy. Simulations are provided to demonstrate the performance of the proposed algorithm in a radial distribution system voltage support application under a range of operating conditions and considering uncertainty in parameter values. The proposed approach and learnings can be extended to a range of network power flow problems and DER-based applications.