شارژ باتری شبکه از وسایل نقلیه الکتریکی Network security-aware charging of electric vehicles

1. Introduction Electric vehicles (EVs) have been receiving considerable attentions worldwide as they are clean and green. However, the large-scale integration of EVs, without coordination, may bring negative impacts on power systems operation, such as lower voltage quality, larger power losses, and more harmonics [1]. Therefore, effective strategies should be developed to schedule the charging of EVs to mitigate the negative impacts and even benefit the grid [2]. In the literatures, studies about EV charging schedule are concentrated on distribution network. Up to now, only a few literatures discussed the charging issues of EVs from the transmission network viewpoint. Ref. [3] presented a bi-level model for coordinating the charging/discharging schedules of EVs. The upper-level model minimizes the system load variance to implement peak load shifting by dispatching each aggregator, and the lower one traces the dispatching scheme determined by the upper-level decision-maker by figuring out an appropriate charging/discharging schedules throughout a specific day. Ref. [4] proposed a multi-objective non-linear mixed integer optimization model for EV charging scheduling considering the uncertainties of photovoltaic and wind power in regional power grids. The fuzzy theory was used to change the multi-objective optimization model into a single-objective non-linear optimization problem. EV charging schedule problems are mostly formulated as optimization issues aiming at improving voltage profile [5–7], flattening load profile [6–10], reducing power losses [7–11], offering ancillary services [12], minimizing the charging cost [13–15], or increasing user satisfaction level [16,17]. Ref. [5] presented a decentralized optimization methodology to coordinate EV charging to facilitate the voltage control on a residential distribution feeder. Ref. [10] presented a methodology to optimize power system demand due to EV charging load, and it was demonstrated that EV charging load has significant potential to flatten the national demand profile in the U.K. Ref. [11] proposed an optimization model considering EV charging demand and voltage constraints to minimize the power losses of distribution systems. Ref. [12] presented a stochastic method for optimal coordination of charging and frequency regulation for an EV aggregator using the Least Square Monte-Carlo technique while modeling electricity price uncertainty. Ref. [15] proposed an intelligent method to control EV charging loads in response to time-of-use price in a regulated market. Ref. [16] proposed a new metric to represent the EV user satisfaction fairness to achieve a tradeoff between the user satisfaction fairness and the total charging cost of electricity. The existing EV charging scheduling methods did not take the N − 1 security constraints into account. However, the secure operation of the system under N − 1 contingency is an essential requirement [18].