Stochastic-Based Optimal Charging Strategy for Plug-In Electric Vehicles Aggregator Under Incentive and Regulatory Policies of DSO

Abstract

The recent increasing penetration of plug-in electric vehicles (PEVs) has provided an opportunity to develop individual PEVs aggregators who benefit from coordinating the vehicles. Optimal self-scheduling of such an aggregator, considering network operation indices, and distribution system operator's (DSO's) policies on the aggregator's performance are the key issues investigated in this paper. The proposed method maximizes the aggregator's daily profit through participating in day-ahead and real-time electricity markets and offering power quality services to DSO in an incentive and regulatory scheme. These services are designed in terms of the daily voltage profile and the power losses cost of the network. The problem is modeled as a two-stage stochastic scheduling problem considering customers’ satisfaction, impacts of uncertainties of driving patterns and real-time market prices, and effects on network operation indices. The model is formulated as a mixed-integer linear programming problem and implemented in GAMS $^{\bigcirc \!\!\!\!{\hbox{R}}}$ software. The technical and financial aspects of the plug-and-play uncoordinated and the proposed coordinated approaches are finally compared for different penetrations of PEVs in a test grid. The results show that applying the proposed strategy can benefit the aggregator in electricity markets and satisfy PEV owners too. Moreover, the reliable and economic operation of the grid can be guaranteed through applying incentive and regulatory policies on the aggregator's performance in high penetration levels.