A processor implemented method, further comprising: obtaining, via one or more hardware processors, an input comprising i) historical data pertaining to generation and demand of energy, ii) historical intraday market data, iii) a specification of a plurality of distributed energy resources DERs connected to a network specific to an aggregator, iv) a preference of one or more subscribers, and v) information associated with the network specific to the aggregator; forecasting, via the one or more hardware processors, a generation and demand of energy by the plurality of DERs for a plurality of delivery slots in an initialized optimization window, based on the input; estimating, via the one or more hardware processors, a two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand of the energy by the plurality of DERs; and executing, an optimization model via the one or more hardware processors, using the estimated two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window to obtain i) an optimal intraday operating schedule for one or more DERs from the plurality of DERs, and, optionally, ii) an intraday bid associated with the plurality of DERs for the plurality of delivery slots to be traded in an intraday market, wherein the step of executing the optimization model comprises: formulating a mixed integer non-linear programming MINLP problem based on the input and the estimated two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window; converting the formulated MINLP problem to an NLP problem; and executing the NLP problem to obtain i) the optimal intraday operating schedule for one or more DERs from the plurality of DERs, and, optionally, ii) the intraday bid associated with the plurality of DERs for the plurality of delivery slots to be traded in an intraday market. obtaining, via one or more hardware processors, an input comprising i) historical data pertaining to generation and demand of energy, ii) historical intraday market data, iii) a specification of a plurality of distributed energy resources DERs connected to a network specific to an aggregator, iv) a preference of one or more subscribers, and v) information associated with the network specific to the aggregator; forecasting, via the one or more hardware processors, a generation and demand of energy by the plurality of DERs for a plurality of delivery slots in an initialized optimization window, based on the input; estimating, via the one or more hardware processors, a two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand of the energy by the plurality of DERs; and executing, an optimization model via the one or more hardware processors, using the estimated two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window to obtain i) an optimal intraday operating schedule for one or more DERs from the plurality of DERs, and, optionally, ii) an intraday bid associated with the plurality of DERs for the plurality of delivery slots to be traded in an intraday market, wherein the step of executing the optimization model comprises: formulating a mixed integer non-linear programming MINLP problem based on the input and the estimated two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window; converting the formulated MINLP problem to an NLP problem; and executing the NLP problem to obtain i) the optimal intraday operating schedule for one or more DERs from the plurality of DERs, and, optionally, ii) the intraday bid associated with the plurality of DERs for the plurality of delivery slots to be traded in an intraday market. formulating a mixed integer non-linear programming MINLP problem based on the input and the estimated two-dimensional 2D distribution of price-volume for the plurality of delivery slots in the initialized optimization window; converting the formulated MINLP problem to an NLP problem; and executing the NLP problem to obtain i) the optimal intraday operating schedule for one or more DERs from the plurality of DERs, and, optionally, ii) the intraday bid associated with the plurality of DERs for the plurality of delivery slots to be traded in an intraday market.