Two-Stage Stochastic Model Using Benders' Decomposition for Large-Scale Energy Resource Management in Smart Grids

The ever-increasing penetration level of renewable energy and electric vehicles threatens the operation of the power grid. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This paper proposes a two-stage stochastic model for a large-scale energy resource scheduling problem of aggregators in a smart grid. The idea is to address the challenges brought by the variability of demand, renewable energy, electric vehicles, and market price variations while minimizing the total operation cost. Benders’ decomposition approach is implemented to improve the tractability of the original model and its computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness of the proposed approach when compared with a deterministic model. They also reveal that demand response and storage systems can mitigate the uncertainty.