بهبود ظرفیت میزبانی از منابع PV-STATCOM با مدیریت بهینه ذخیره¬ساز انرژی و در نظر گرفتن برنامه پاسخگویی بار
محورهای موضوعی : مهندسی برق و کامپیوتر
فرزین فردین فر
1
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مصطفی جعفری کرمانی پور
2
1 - دانشکده مهندسي برق، دانشگاه شهید باهنر، کرمان، ايران
2 - دانشکده مهندسي برق، دانشگاه ملی مهارت، تهران، ايران
کلید واژه: ظرفیت میزبانی, برنامه پاسخگویی بار, الگوریتم بهینه¬سازی آکیولا, روش نمونه¬برداری ابرمکعب لاتین, جبران¬ساز سنکرون استاتیکی خورشیدی,
چکیده مقاله :
در سالیان اخیر به دلیل افزایش نرخ نفوذ سیستمهای فتوولتائیک (PV)در شبکههای توزیع، چالشهای فنی زیادی بهوجود آمده است. یکی از پیامدهای مخرب ناشی از نصب زیاد سیستمهای PV، اضافهولتاژ در برخی از ساعات روز است که این عامل باعث کاهش ظرفیت میزبانی(HC) شبکه از منابع فتوولتائیک میشود. نوآوری این مقاله اجرای یکپارچه برنامهپاسخگویی بار، استفاده از ذخیرهسازهای انرژی و اینورترهای هوشمند سیستمهای فتوولتائیک به عنوان جبرانکننده سنکرون استاتیکی (PV-STATCOM) میباشد که از آنها برای بهبود ظرفیت میزبانی استفاده شده است. در این راستا، مدیریت مناسب ذخیرهساز انرژی، کنترل توان راکتیو مبادله شده با شبکه توسط PV-STATCOM به منظور بهینهسازی تابع هدف شامل بیشینهسازی ظرفیت میزبانی، کمینهسازی تلفات، انحراف ولتاژ و هزینههای بهرهبرداری صورت گرفته است. همچنین جهت واقعی شدن محاسبات و شبیهسازیهای انجام شده از توابع احتمالاتی برای مدلسازی عدم قطعیت بار در شبکه توزیع نیز استفاده شده است. مدلسازی عدم قطعیت انجام شده در این مقاله، نمونه برداری به روش ابر مکعب لاتین (LHS) است که سرعت و همپوشانی نمونهبرداری بهتری نسبت به روش مونت کارلو دارد. برای حل مساله بهینه سازی تابع هدف، از الگوریتم بهینهسازی عقاب آکیولا (AO) استفاده شده و نتایج بدست آمده با الگوریتمهای تجمعی ذرات (PSO) و ژنتیک (GA) مقایسه شدهاند. شبیهسازی روش پیشنهادی بر روی شبکه تست 15 باسه IEEE، تاثیرگذاری آن بر افزایش ظرفیت میزبانی تا 39 درصد را ضمن رعایت حدود مجاز تغییرات ولتاژ و بهبود تلفات و هزینه بهرهبرداری، نشان میدهد.
In recent years, due to the increasing penetration of photovoltaic (PV) systems in distribution networks, there are several technical challenges. One of the destructive consequences of extra PV system installation is voltage raising in some hours of the day, which reduces Hosting Capacity (HC) of the grid. The innovation of this paper is the integrated implementation of the demand response program, utilization of energy storages and smart inverters as static synchronous compensation (PV-STATCOM), which they are used for improving Hosting Capacity. In this regard, proper energy storage management, reactive power control by PV-STATCOM in order to optimize the objective function include maximizing the Host Capacity, minimization of losses, voltage deviation and operation cost. Also, the real computations are performed by using probabilistic functions to model the load uncertainty in distribution network. The uncertainty quantification model in this paper is Latin Hypercube Sampling (LHS) method, which has better speed calculation and sampling compare to Monte Carlo method. To solve the optimization problem of objective function, the optimization algorithm of Aqiula Optimizer (AO) is used and the results are compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed method is based on IEEE 15-bus test system and shows its effectiveness on increasing Hosting Capacity up to 39 % while respecting to the limits of voltage variation and improvement of losses and costs.
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