A Support Vector Regression Based Model Predictive Control for Volt-Var Optimization of Distribution Systems

This paper proposes a support vector regression (SVR)-based model predictive control (MPC) for the volt–var optimization (VVO) of electrical distribution systems. First, measurement data from a few days of operation of a distribution system, gathered using advanced metering infrastructure (AMI), are used to train an SVR model of the system. The trained model is then employed by the MPC in a closed-loop control scheme to control capacitor banks and tap changers of the distribution system so that the power loss is minimized, and voltage profiles are maintained within a specific range. In contrast to the many existing VVO methods, the proposed scheme does not require any circuit-based simulations for its operation, nor does it assume that the distribution system is radial. The simulation results of applying the proposed SVR-based MPC to IEEE123 bus test feeder proves that despite its measurement-based feature, the proposed approach is capable of providing close to optimal solutions to the VVO problem. The simulation results also suggest a satisfactory outcome of the proposed approach in controlling meshed grids or in the presence of distributed energy resources (DERs).