Two‐parameter Kautz network‐based LTV‐MPC for non‐linear standalone micro‐grid control

This study proposes a novel linear time‐variant model predictive controller (LTV‐MPC) for the centralised control of non‐linear standalone micro‐grids. At each sample, within the prediction horizon, LTV‐MPC linearises the non‐linear micro‐grid model around the state and input reference trajectories resulting in a linear time‐variant (LTV) model. The LTV model is used for predicting the forced response of the micro‐grid. The natural response is predicted by solving the non‐linear model along the state and input reference trajectories. An optimal control problem for the LTV‐MPC is formulated using the complete predicted response, which is a quadratic programming problem instead of a non‐convex non‐linear programming problem. The quadratic programming problem is solved online at each sample to generate the optimal control trajectories within the control horizon. The study recommends the use of two‐parameter orthonormal Kautz networks in the LTV‐MPC design for the control trajectories approximation. The approximation drastically reduces the number of optimising variables in the optimal control problem without compromising LTV‐MPC performance. A standalone eight bus micro‐grid with one synchronous distributed generator (DG) and one photovoltaic‐DG is considered for the analysis. The LTV‐MPC performance is assessed for the different load disturbance and source intermittency scenarios. The results are compared with the existing MPC designs.