Model Predictive for Reactive Power Scheduling Control Strategy for PV–Battery Hybrid System in Competitive Energy Market

Abstract

This article presents an optimal control scheme based on model predictive control (MPC) strategy for a photovoltaic (PV)/battery hybrid system. By considering various trading scenarios of electricity markets in order to maximize profits, an objective function is formulated using variable electricity tariff (VET), battery state of charge (SoC), and availability of solar insolation. The proposed optimal scheme is designed with a two-stage control algorithm based on VET, forecasted solar insolation, and SoC to easily cope with the load demand variation between low and high prices. In this case, the MPC is implemented to predict the SoC in the PV–battery hybrid system by directly calculating it in the first stage while predicting the values of solar irradiance, load consumption, and electricity price. Furthermore, the proposed MPC is used to account for forecasting errors and acts under the given limits to minimize errors and compensate reactive power during the scheduling process. To validate the performance of the proposed control scheme, the PV/battery system is modeled and implemented in MATLAB/Simulink package. Simulation results demonstrate that the proposed optimal control scheme has better performances under various scenarios such as parameter uncertainties and sudden changes.