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This work focuses on the development of a supervisory model predictive control method for the optimal management and operation of hybrid standalone wind-solar energy generation systems. We design the supervisory control system via model predictive control which computes the power references for the wind and solar subsystems at each sampling time while minimizing a suitable cost function. The power references are sent to two local controllers which drive the two subsystems to the requested power references. We discuss how to incorporate practical considerations, for example, how to extend the life time of the equipment by reducing the peak values of inrush or surge currents, into the formulation of the model predictive control optimization problem. We present several simulation case studies that demonstrate the applicability and effectiveness of the proposed supervisory predictive control architecture.