This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.