مدیریت احتمالی تراکم با در نظر گرفتن عدم قطعیت‌های سيستم قدرت و استفاده از الگوریتم برنامه‌ریزی مبتنی بر شانس
محورهای موضوعی : مهندسی برق و کامپیوترمهرداد حجت 1 * , محمدحسین جاویدی 2
1 - دانشگاه فردوسی مشهد
2 - دانشگاه فردوسی مشهد
چکیده مقاله :
تراكم در خطوط انتقال يكي از موانع اصلي براي شكل‌گيري رقابت سالم در بازار برق مي‌باشد و بنابراين تحقيقات متعددي بر روي روش‌هاي مديريت تراكم در بازار برق انجام شده است. از سوي ديگر، رفتار يك سيستم قدرت داراي ماهيت تصادفي است و به‌همين دليل در بسياري از مباحث مطالعاتی مرتبط با بهره‌برداری و برنامه‌ریزی، سیستم به‌صورت غير قطعي مدل‌سازي و بررسي مي‌گردد. عدم قطعيت‌هاي سيستم قدرت را به‌طور كلي مي‌توان در سه بخش مستقل بار، توليد و شبكه انتقال بررسي نمود. در اين مقاله، هدف ارائه روشي جديد براي تحليل تصادفي تراكم به كمك مدل‌سازي عدم قطعيت‌هاي ذاتي سيستم قدرت است. جهت تحليل مسئله مديريت تراكم به‌صورت احتمالي به‌جاي استفاده از روش‌هاي معمول، از برنامه‌ريزي مبتني بر شانس كه روشي براي مدل‌سازي مسایل بهينه‌سازي تصادفي است، استفاده مي‌گردد. مدل پیشنهادی مدیریت احتمالی تراکم توسط يك روش عددي با تكيه بر الگوريتم ژنتيك كد حقيقي و تكنيك مونت كارلو تحليل مي‌شود. براي مطالعه كارايي روش پيشنهادي، مديريت تراكم به‌صورت احتمالي بر روي شبكه 9باسه اصلاح‌شده IEEE پياده‌سازي مي‌شود. در اين شبكه منتخب، روش پيشنهادي براي تحليل تصادفي تراكم با روش ميانگين مقايسه شده و عملكرد روش ارائه‌شده در اين مقاله ارزيابي مي‌گردد. مطالعه نتایج، نشان‌دهنده انعطاف‌پذیری روش پیشنهادی در مدیریت تراکم شبکه انتقال است.
In this paper, a new method for probabilistic congestion management considering power system uncertainties is proposed. Chance constrained programming (CCP) is used to formulate the probabilistic congestion management as an efficient approach for stochastic optimization problems. The CCP based probabilistic congestion management is solved utilizing a numerical approach by applying the Monte-Carlo technique into the real-coded genetic algorithm. The effectiveness of the proposed method is evaluated applying the method to the modified IEEE 9-bus test system. The results of the proposed approach are compared with those of the expected method to have a comprehensive study. The simulation results reflect the flexibility of the proposed approach in transmission congestion management.
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