مقاله انگلیسی رایگان در مورد برنامه ریزی شارژ تخلیه وسایل نقلیه الکتریکی سیستم مدیریت انرژی ساختمان

elsevier

 

مشخصات مقاله
عنوان مقاله  A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a  building energy management system
ترجمه عنوان مقاله  یک الگوریتم اکتشافی مبتنی بر برنامه نویسی خطی برای برنامه ریزی شارژ و تخلیه وسایل نقلیه الکتریکی در سیستم  مدیریت انرژی ساختمان
فرمت مقاله  PDF
نوع مقاله  ISI
نوع نگارش مقاله مقاله پژوهشی (Research article)
سال انتشار

مقاله سال ۲۰۱۷

تعداد صفحات مقاله  ۸ صفحه
رشته های مرتبط  مهندسی برق
گرایش های مرتبط  ماشینهای الکتریکی
مجله

 مجله امگا – Omega

دانشگاه  دانشگاه اوزاکا، سویتا، اوزاکا، ژاپن
کلمات کلیدی  تامین برق، وسیله نقلیه الکتریکی، سیستم مدیریت انرژی ساختمان، برنامه ریزی خطی، شبکه زمان فضا
کد محصول  E4422
نشریه  نشریه الزویر
لینک مقاله در سایت مرجع  لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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۱٫ Introduction

With rising greenhouse gas emission and gasoline prices, electric vehicles (EVs) are becoming an attractive alternative to gasoline vehicles. However, the rapid growth in electricity demand may cause large and undesirable peak loads in the power grid. Fortunately, EVs can flexibly coordinate charge schedules, and most owners will not be inconvenienced, providing that the EV batteries are full before departure. A wide variety of models and algorithms has been proposed for charge scheduling of EVs. Clement et al. [2] proposed a quadratic programming model to minimize power losses and voltage deviations. Deilami et al. [4] reported a fast heuristic algorithm, called the maximum sensitivities selection algorithm, to minimize the total cost involved with the additional electricity demands of EVs and power losses. Sortomme et al. [22] described and compared three optimization models; minimizing power losses, minimizing load variance, and maximizing load factor. Soares et al. [21] proposed a linear programming (LP) model that minimizes deviations between expected and actual demands, which was suitable for quasi-real time applications because of its low computational cost. HernánedzArauzo et al. [11] formulated the scheduling problem as a sequence of constraint satisfaction problems (CSPs) over time called the dynamic CSP, and decomposed each CSP into three instances of a one-machine scheduling problem. Kim et al. [17] analyzed performance measures of two typical charge scheduling methods: the first-in-first-out and the processor sharing under a realistic stochastic model for EV battery charging stations.

EVs can offer further benefits to the power grid by discharging electricity from their batteries, which is called vehicle-to-grid power [15,16]. The rapid growth of intermittent renewable energy sources, such as photovoltaic and wind power generation, requires huge number of battery storages for stabilizing the large fluctuations in the power grid. EV batteries are expected to provide an alternative to expensive stationary battery storages and to play an important role in the emerging power grid that has a large number of renewable energy sources.

Several optimization models and algorithms have been proposed for charge and discharge scheduling of EVs. Han et al. [8] reported a dynamic programming algorithm to optimize frequency regulation, in which charge and discharge scheduling of individual EVs was considered rather than that of multiple EVs. Clement et al. [3] proposed an LP model and He et al. [10] proposed a quadratic programming model to minimize the total charge cost. Zakariazadeh et al. [26] formulated a multi-objective model to minimize operational costs and emissions as a mixed integer nonlinear programming (MINLP) model and it solved with Bender’s decomposition technique. Kawashima et al. [14] reported a mixed integer linear programming (MILP) model to minimize a total charge cost, and García-Villalobos et al. [7] presented a comprehensive review of models and algorithms for charge and discharge scheduling of EVs.

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