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Power network operators have recently faced new challenges due to an increase in the penetration of non-dispatchable renewable energy sources in power grids.Incorporating emerging flexible resources like electric vehicle parking lots (EVPLs) and demand response programs (DRPs) into power systems, could be a good solution to deal with inherent uncertainties imposed by these resources to the power grid. EVPLs can improve power system operating conditions by active and reactive power injection capabilities. The participation of consumers in DRPs can also improve energy consumption management by decreasing or shifting loads to other periods. This paper proposes a hybrid information gap decision theory (IGDT)stochastic method to solve a transmission-constrained AC unit commitment model integrated with electric vehicle (EV), incentive-based DRP, and wind energy. The behavioural uncertainty related to EV owners is modelled using a scenario-based method. Additionally, an IGDT method is applied to manage wind energy uncertainty under a two-level optimization model. Verification of the proposed model is done under several case studies. Based on the results achieved, the proposed risk-based hybrid model allows the operator to differentiate between the risk level

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SUMMARYPlug-in hybrid electric vehicles (PHEVs) are becoming more prevalent day by day. The batteries of these electric vehicles may be charged from a standard outlet at home. These extra electrical loads have several impacts on distribution networks, e.g. network energy loss. This paper presents a convex quadratic local optimal solution, instead of non-convex global optimal approach, to minimize the energy loss of distribution system with the different penetration levels of PHEVs. In this paper, it is assumed that vehicle owners can charge their vehicles either at workplace or at home. Furthermore, daily needed energy of PHEVs is simulated based on stochastic modeling of PHEV owners' behavior at any time. Moreover, in this paper it is supposed that PHEVs can be used as reactive power resources in vehicle to grid (V2G) operation mode. The proposed methodology is applied to a realistic distribution network. The results show that network energy loss may be remarkably increases, as the penetration of PHEVs increases without smart charging strategy. Managed and smart charging of PHEVs is needed to minimize the extent of network energy loss. As revealed by the results, smart charging of PHEVs with consideration of V2G operation mode in conjunction with charging at workplace may have the most effect on the network energy loss reduction.