A model predictive control approach for matching uncertain wind generation with PEV charging demand in a microgrid

The matching between uncertain wind power supply and plug-in electric vehicles (PEVs) charging demand has the potential to reduce the greenhouse gas emission and the fossil fuel pollution. In view of this, we propose a hierarchical model predictive control approach to coordinate the wind generation and PEV charging in the context of microgrid. The proposed control approach consists of two control layers. In the top layer, a stochastic model predictive controller computes the optimal power references for the wind generator and the PEV fleet. These references are fed to the bottom layer, and are further executed by the wind generator controller and PEV fleet controller, respectively. A salient feature of this approach is that it comprehensively incorporates the uncertainties in both sides of supply and demand, i.e., the uncertainties associated with the maximum available wind generation, and the uncertainties associated with the PEVs charging demand. Using the Chebyshev inequality and the chance constraints, the corresponding stochastic optimization problem is approximated as a quadratic programming problem. By doing so, the proposed approach not only keeps the microgrid power balance, but also ensures the PEV users' quality of experience. Furthermore, it can bring the power flow between microgrid and utility power system to a predefined trajectory. Simulation results based on real-world wind and PEV data validate the effectiveness of the proposed approach.