بررسی جایابی خازن در بارهای مختلف برای کاهش توان تلفاتی سیستم توزیع با استفاده از الگوریتم برنامه ریزی خطی و درجه بندی دوباره بارها

محورهای موضوعی : انرژی های تجدیدپذیر

علی احمدپور ¹ (گروه مهندسی برق قدرت ، دانشکده فنی و مهندسی ،دانشگاه محقق اردبیلی، اردبیل ، ایران.)
حسین شایقی ² (هیات علمی گروه مهندسی برق قدرت ، دانشکده فنی و مهندسی ،دانشگاه محقق اردبیلی، اردبیل ، ایران.)
الهام مکرمیان ³ (گروه مهندسی برق قدرت ، دانشکده فنی و مهندسی ،دانشگاه محقق اردبیلی، اردبیل ، ایران.)

تاریخ دریافت : 1397/01/10 تاریخ پذیرش : 1397/09/25 تاریخ انتشار : 1397/11/15

کلید واژه: کاهش تلفات, شبکه های توزیع, جایابی خازن, بارهای متغیر, برنامه ریزی اعداد صحیح آمیخته,

چکیده مقاله :

شبکه های توزیع برق امروزه دارای بارهای مختلف و متغیری هستند که هرکدام الگوهای مصرف مخصوص به خود را دارند. این بارهای هرکدام دارای توان تلفاتی مشخصی هستند. یکی از راه های موثر برای کاهش توان تلفات و افزایش بازده در شبکه های توزیع برق، استفاده از جایابی خازن در پست های برق است. در این مقاله جایابی خازن در طرف ثانویه ترانسفورماتورهای شبکه توزیع انجام می شود. روش ریاضی برنامه ریزی اعداد صحیح برای جایابی خازن به منظور افزایش سود خالص حاصل از کاهش تلفات استفاده می شود. با توجه به تغییر اندازه بارها، یک الگوی ویژه برای مدل سازی انواع مختلف بارهای متغیر استفاده می شود. علاوه بر بررسی مشکل بارهای ثابت، روش پیشنهادی برای سه نوع بار صنعتی، مسکونی و تجاری مورد آزمایش قرار گرفته است و نتایج به دست آمده از همه زمان ها بر کاهش ضرر و افزایش سود مالی پروژه برای درجه بندی دوباره همه بارها مورد بحث قرار گرفته است .

چکیده انگلیسی:

Capacitor placement at the substation is one of the effective methods for loss reduction and efficiency increasing of the power systems. In this paper, a novel method for capacitor placement on the secondary side of the distribution transformers is carried out. The mathematical method of Mixed–Integer Linear Programming (MILP) algorithm is used for paper goal to increase the Net Present Value (NPV), resulting from the loss reduction. Due to the variability of the loads, a special template is used for loads modelling. Not only the constant loads, but also three types of industrial, residential and commercial loads are assessed with the proposed method. Then, the results are discussed in order to re-grade loads. Also, the proposed method are compared with two methods as Particle Swarm Optimization (PSO) algorithm and Teaching–Learning Based Optimization (TLBO) algorithm. All of the tests are simulated with MATLAB software and total time of problem is considered as ten years.

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