Probabilistic Storage Modeling and Suboptimal Sizing of Renewable Energy Microgrids

This paper proposes a novel probabilistic storage modeling and a suboptimal sizing of renewable energy Micro-grids. The emergence of renewable energies and distributed systems has been worldwide hastened. Yet, several challenges face this metamorphosis, above all, renewable energy uncertainties which require the integration of storage systems. When the system is undersized, the renewable energy would be dumped, and when it is oversized, the storage would be ineffective. Optimal configurations with probabilistic constraints are then primordial. Therefore, the contribution of this paper is twofold: First, it formulates a novel modeling of energy storage systems by introducing the idleness and counterbalance probabilities; and second, it proposes a suboptimal sizing by maximizing the goodness of fit between the probability density functions of residual power, or the difference between the generation and consumption, and the matching storage output power. Simulations were operated on two Microgrids with distinct parameters, and optimization computing used the Genetic Algorithm toolbox of Matlab.