Stochastic-Predictive Energy Management System for Isolated Microgrids


This paper presents the mathematical formulation and management design of a stochastic-predictive energy management system for isolated microgrids. The proposed strategy addresses uncertainty employing a 2-stage decision method combined with a receding horizon approach. The first stage call variables (unit commitment) are determined using a stochastic mixed-integer linear programming formulation, whereas the second stage variables (optimal power flow) are refined using a nonlinear programming formulation. This novel approach was tested on a changed CIGRE check system under different configurations comparing the results with respect to a deterministic approach. The results show the appropriateness of the tactic to account for uncertainty in the power forecast.

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