Talari, SaberShafie-khah, MiadrezaChen, YueWei, WeiGaspar, Pedro DinisCatalão, J. P. S.2019-10-182019-10-182018http://hdl.handle.net/10400.6/7286In this paper, a new methodology to unleash the potential of demand response (DR) in real-time is presented. Customers may tend to apply their DR potential in the real-time market in addition to their scheduled potential in the day-ahead stage. Thus, the proposed method facilitates balancing the realtime market via DR aggregators (DRAs). It can be vital once the stochastic variables of the network such as production of wind power generators (WPG) do not follow the forecasted production in real-time and have some distortions. Two-stage stochastic programming is employed to schedule some DR options in both day-ahead and real-time markets. DR options in real-time are scheduled based on possible scenarios that reflect the behaviors of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study and in the IEEE RTS-96, which shows a reduction in total operation cost.engOptimal demand responseReal-time marketTwo-stage stochastic programmingUncertainty handlingWind power generationjournal article10.1109/TSTE.2018.2868449