سیستم جامع مدیریت بهینه منابع تولید پراکنده با استفاده از شبکه عصبی دینامیکی در مدل‌سازی عدم قطعیت مصرف انرژی الکتریکی ریزشبکه‌های متصل به شبکه
محورهای موضوعی : مهندسی برق و کامپیوترمحمد ویسی 1 , محمدرضا سلطانپور 2 , جعفر خلیل پور 3 , هادی نیایی 4
1 - دانشگاه پدافند هوایی خاتم‌الانبیاء (ص)
2 - دانشگاه هوايي شهيد ستاري
3 - دانشگاه خاتم الانبیا
4 - دانشگاه تبریز
کلید واژه: ریزشبکهشبکه بالادستسیستم همسایگیقابلیت اطمینانشبکه عصبی دینامیکی,
چکیده مقاله :
در این مقاله، یک استراتژی برای به دست آوردن برنامه‌ریزی بهینه به منظور مدیریت توان الکتریکی ریزشبکه‌ها، با اشتراک‌گذاری توان الکتریکی از طریق هماهنگی میان ریزشبکه‌ها و سیستم همسایگی، که هیچ هزینه اضافی برای واحدهای تولیدی ندارد، پیشنهاد شده است. میزان عدم قطعیت بار مصرف‌کنندگان، بر اساس شبکه عصبی دینامیکی و با توجه به روند پیاده‌سازی و دقت بالای پیش‌بینی، مدل می‌شود. از نگاه دیگر، برای تأمین انرژی الکتریکی ریزشبکه، علاوه بر اتصال به شبکه بالادست، از دیزل‌ژنراتور و انرژی‌های تجدیدپذیر مانند انرژی خورشیدی، انرژی بادی و باتری ذخیره‌ساز انرژی الکتریکی استفاده شده است. همچنین استفاده از فاکتورهای قابلیت اطمینان به همراه ارزیابی دقیق هزینه‌های جاری، موجب بهبود کارایی ریزشبکه می‌شود. از این رو، احتمال میزان بار تأمین‌نشده سیستم (LPSP) و احتمال تأمین‌نشدن انرژی مورد انتظار مصرف‌کنندگان شبکه انرژی الکتریکی (LOLE)، به عنوان فاکتورهای ارزیابی دقت هزینه‌های جاری مطرح می‌شوند. مدل پیشنهادی در نرم‌افزارهای GAMS و MATLAB پیاده‌سازی شده و نتایج حاصل نشانگر عملکرد مطلوب الگوریتم پیشنهادی بوده و موجب سود‌دهی سیستم مورد مطالعه می‌شود.
In this paper, to enhance the optimal planning for power management of micrigrids, a strategy is proposed using power sharing through coordination between microgrids and the neighborhood system, which has no additional costs for generating units. The uncertainty values of electrical consumers are modeled by dynamic neural network, considering the implementation process and high accuracy of forecasting. In another view, to supply the electrical energy of microgrid, diesel generator, renewable energies such as solar energy and wind energy and so, battery energy storage are used, in addition to the upstream grid connection. As well as, using of the reliability factors, along with a detailed assessment of current costs will improve the performance of microgrid. Hence, the loss of power supply probability (LPSP) and loss of load expectations (LOLE) are expressed as factors for assessing the accuracy of current costs. The proposed model is implemented in GAMS and MATLAB environment and the simulation results clearly demonstrate the desired performance of the proposed algorithm, and leads to gaining revenue for the under-study system.
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