Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

被引:77
|
作者
Shojaabadi, Saeed [1 ]
Abapour, Saeed [1 ]
Abapour, Mehdi [1 ]
Nahavandi, Ali [2 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, POB 51666-15813, Tabriz, Iran
[2] Malayer Univ, Fac Engn, Malayer, Iran
关键词
Distribution network; PHEVs charging station planning; Wind power generation planning; Distribution system manager; Wind generation owner; System uncertainties; INTELLIGENT PARKING LOT; RENEWABLE ENERGY; GENETIC ALGORITHM; INTEGRATION; MANAGEMENT; SYSTEMS; OPTIMIZATION; SERVICES; MODEL; V2G;
D O I
10.1016/j.renene.2016.06.032
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:237 / 252
页数:16
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