جایابی و تعیین ظرفیت بهینه منابع انرژی تجدیدپذیر و ایستگاه شارژ خودروی برقی به صورت همزمان با استفاده از الگوریتم بهینه‌سازی GA-PSO
محورهای موضوعی : مهندسی برق و کامپیوتر
محمدحسن مرادی
1
(دانشگاه بوعلی سینا همدان)
مصطفی رضایی مظفر
2
(دانشگاه آزاد اسلامی واحد همدان)
پرهام محمد علیزاده
3
(دانشگاه آزاد اسلامی واحد قزوین)
کلید واژه: بهینه‌سازی خودروی برقی ایستگاه شارژ منابع انرژی تجدیدپذیر الگوریتم ترکیبی GA-PSO,
چکیده مقاله :
نفوذ منابع انرژی تجدیدپذیر (RES) و خودروهای برقی به شبکه به دلیل ماهیت تصادفی‌بودنشان می‌تواند تأثیرات منفی بر عملکرد شبکه مثل کاهش کیفیت توان و افزایش تلفات داشته باشد. این چالش‌ها می‌بایست با برنامه‌ریزی دقیق بر مبنای تغییرات خروجی این منابع برای تأمین تقاضای اضافی ناشی از شارژ خودروها به حداقل برسد. به این منظور در این مقاله روشی جدید برای جایابی و تعیین ظرفیت همزمان منابع تجدیدپذیر و ایستگاه شارژ خودروهای برقی و مدیریت پروسه شارژ خودروها در شبکه ارائه شده است. تابعی چند هدفه در جهت کاهش تلفات توان، نوسانات ولتاژ، هزینه تأمین انرژی و هزینه تعمیر و نگهداری باتری خودرو معرفی شده که در آن یافتن مکان و ظرفیت منابع تجدیدپذیر و ایستگاه شارژ خودروی برقی به عنوان متغیرهای هدف انجام می‌گیرد. ضرایبی وابسته به سرعت باد، تابش خورشید و نسبت تقاضای پیک سیستم برای بهبود ضریب بار شبکه و مدیریت الگوی شارژ خودروها در ساعات پیک و غیر پیک معرفی شده است. الگوریتم بهینه‌سازی ترکیبی GA-PSO بهبودیافته برای حل مسئله بهینه‌سازی در چهار سناریو مختلف استفاده شده و عملکرد روش مذکور با شبیه‌سازی بر روی شبکه تست IEEE 33باسه در نرم‌افزار Matlab بررسی شده است.
Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, and voltage deviations. These challenges must be minimized by optimal planning based on the variable output from RES to meet the additional demand caused by EV charging. In this paper, a novel method for optimal locating and sizing of RES and EV charging stations simultaneously and managing vehicle charging process is provided. A multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost. In this optimization problem, the location and capacity of RES and EV charging stations are the objective variables. Coefficients which are dependent on wind speed, solar radiation, and hourly peak demand ratio for the management of the EV charging pattern in low load hours are introduced. GA-PSO hybrid improved optimization algorithm is used to solve the optimization problem in five different scenarios. The performance of the proposed method on IEEE 33-bus system has been investigated to validate the effectiveness of the novel GA-PSO method to optimal sitting and sizing of RES and EV charging stations simultaneously
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