4.7 Article

Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation

Journal

ENERGY CONVERSION AND MANAGEMENT
Volume 100, Issue -, Pages 262-276

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2015.05.015

Keywords

Plug-in hybrid electric vehicle (PHEV); Adequacy evaluation; Monte Carlo simulation (MCS); Risk assessment; Smart grids; Uncertainty; Managed charging; Vehicle-to-grid (V2G); Well-being assessment; Renewable resource; Solar distributed generation (DG) units; Wind turbine DG units; Diesel DG units

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The unmanaged charging of plug-in-hybrid-electric vehicles (PHEVs) may adversely affect electric grid reliability because a large amount of additional electrical energy is required to charge the PHEVs. In this paper, a comprehensive method to evaluate the system reliability concerning the stochastic modeling of PHEVs, renewable resources, availability of devices, etc. is proposed. In addition, a novel risk management method in order to reduce the negative PHEVs effects is introduced. This method, which consists of managed charging and vehicle-to-grid (V2G) scenarios, can be practically implemented in smart grids because the bidirectional-power-conversion technologies and two-way of both the power and data are applicable. The introduced method was applied to a real 20 kV network of the Hormozgan Regional Electric Company (HREC) of Iran which is considered as a pilot system for upgrading to smart distribution grid. The results showed that the smart grid's adequacy was jeopardized by using the PHEVs without any managed charging schedule. The sensitivity analyses results illustrated that by using the risk management scenarios, not only did the PHEVs not compromise the system reliability, but also in the V2G scenario acted as storage systems and improved the well-being criteria and adequacy indices. The comparison between the results based on the proposed method and the other conventional approaches in addition to study of various parameters uncertainty emphasized the advantages of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

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