Distributed Model Predictive Control Of Wind And Solar Generation System

Distributed model predictive control for a hybrid system that comprises wind and photovoltaic generation subsystems, a battery bank and an ac load is developed in this paper. Consider that the wind subsystem and the solar subsystem are two spatial distributed energy generation systems,and distributed model predictive control algorithm fits in well, so we design a distributed MPC for optimal management and operation of distributed wind and solar energy generation system. The wind and solar generation system is characterized by nonlinearity. So it can not apply distributed model predictive control algorithm to the wind and solar generation system. Therefore, neural model is used to approximating the dynamics of nonlinear process, so we using neural network to train a neural network model of each subsystem. Using the set point linearization, we can get the linear distributed model of each subsystem. Reasonable solution to the optimization and constraints by using distributed model predictive control is presented. We design the objective function, and satisfy the corresponding constraints, at last, by quadratic programming to solve the optimal value. The performance of the distributed model predictive control is show through computer simulation to illustrate the advantages of the distributed MPC method.