مدل سازی برنامه های پاسخگویی بار در مدیریت چندگانه انرژی و ساختار در ریزشبکه های مجهز به تولیدکننده همزمان برق و حرارت

محورهای موضوعی : انرژی های تجدیدپذیر

مجید زارع ¹ , سید امین سعید ² , حمیدرضا اکبری ³

1 - گروه مهندسی برق- واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران
2 - گروه مهندسی برق- واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران
3 - گروه مهندسی برق- واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران

تاریخ دریافت : 1400/07/24 تاریخ پذیرش : 1400/11/18 تاریخ انتشار : 1402/03/01

کلید واژه: کاهش هزینه‌ها, ریزشبکه, مدیریت انرژی و ساختار, برنامه پاسخ‌گویی بار,

چکیده مقاله :

مدیریت انرژی در ریزشبکه ها با اهداف متفاوتی نظیر کاهش هزینه های بهره برداری انجام می شود. در این رویکرد، بهره بردار ریزشبکه با تعیین میزان تولید انرژی توسط هر یک از منابع انرژی در دسترس، سعی در مدیریت انرژی به منظور تامین انرژی مورد نیاز مصرف کنندگان با حداقل هزینه ممکن دارد. این منابع می توانند تولیدکننده انرژی الکتریکی، حرارتی یا ترکیبی باشند. در این مقاله مدیریت انرژی و ساختار در ریزشبکه با اهدافی نظیر کاهش هزینه های بهره برداری، کاهش آلودگی های زیست محیطی و بهبود شاخص های فنی نظیر کاهش تلفات توان حقیقی صورت گرفته است. به این منظور از ابزارهایی مانند برنامه های پاسخ گویی بار، ذخیره سازها، منابع تولید پراکنده مانند تولیدکننده های همزمان برق و حرارت، منابع تجدیدپذیر مانند واحدهای بادی و فتوولتاییک و بازآرایی فیدرهای ریزشبکه بهره گرفته شده است. از الگوریتم ژنتیک که از معتبرترین الگوریتم های فراابتکاری است برای حل مسئله بهینه سازی مدنظر استفاده شده است. نتایج عددی کارایی مدل ارائه شده را نشان می دهد.

چکیده انگلیسی:

Energy management in microgrids is done with different goals such as reducing operation costs. In this approach, the microgrid operator tries to manage energy in order to supply the energy required by consumers at the lowest possible cost, by determining the amount of energy generation by each of the available energy sources. These resources can generate electrical, thermal or combined energy. In this paper, energy and structure management in microgrids have been done with objectives such as reducing operation costs, reducing environmental pollution and improving technical indices such as reducing real power losses. For this purpose, it has been used from options such as demand response programs, storage devices, distributed generation resources such as combined heat and power generators, renewable sources such as wind and photovoltaic units and feeder reconfiguration. In this paper, the genetic algorithm, which is one of the most valid meta-heuristic algorithms, is used to solve the optimization problem. Numerical results show the efficiency of the proposed model.

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