stochastic multiperiod decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles

this paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (evs). the operational framework is a two-stage programming model in which at the first stage, the retailer and ev aggregator do their medium-term planning. determination of retailer's optimum selling price and the amount of energy that should be purchased from bilateral contracts are medium-term decisions that are made one month prior to real-time market. at the second stage, market agents deal with their activities in the short-term period. in this stage the retailer may modify its preliminary strategy by means of pool market option, interruptible loads (ils), self-scheduling and evs charging and discharging (v2g). thus, a bi-level programming is introduced in which the upper sub-problem maximizes retailer profit, whereas the lower sub-problem minimizes the aggregated evs charging and discharging costs. final decision making is obtained in this stage that may be considered as a day-ahead market, keeping in mind the medium-term decisions. due to the volatility of pool price and uncertainties associated with the consumers and evs demand, the proposed framework is a mixed integer nonlinear stochastic optimization problem; therefore, monte carlo simulation (mcs) is applied to solve it. furthermore, a market quota curve is utilized to model the uncertainty of the rivals and obtaining retailer's actual market share. finally, a case study is presented in order to show the capability and accuracy of the proposed framework.