A stochastic model for energy resources management considering demand response in smart grids

•A new stochastic model for energy scheduling tackling several uncertainty sources.•The sources of uncertainty are: load demand, EVs, renewables and market price.•The two-stage stochastic programming approach is able to tackle the developed model.•Realistic large-scale scenarios with high penetration of distributed resources.•Results suggests that demand response mitigates the sources of uncertainty. Renewable energy resources such as wind and solar are increasingly more important in distribution networks and microgrids as their presence keeps flourishing. They help to reduce the carbon footprint of power systems, but on the other hand, the intermittency and variability of these resources pose serious challenges to the operation of the grid. Meanwhile, more flexible loads, distributed generation, and energy storage systems are being increasingly used. Moreover, electric vehicles impose an additional strain on the uncertainty level, due to their variable demand, departure time and physical location. This paper formulates a two-stage stochastic problem for energy resource scheduling to address the challenge brought by the demand, renewable sources, electric vehicles, and market price uncertainty. The proposed method aims to minimize the expected operational cost of the energy aggregator and is based on stochastic programming. A realistic case study is presented using a real distribution network with 201-bus from Zaragoza, Spain. The results demonstrate the effectiveness and efficiency of the stochastic model when compared with a deterministic formulation and suggest that demand response can play a significant role in mitigating the uncertainty.