دانلود مقاله ISI انگلیسی شماره 56946
عنوان فارسی مقاله

مدیریت انرژی توسط برنامه ریزی مجدد ژنراتور در سیستم قدرت آزادشده احتقانی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
56946 2016 15 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Energy management by generator rescheduling in congestive deregulated power system
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Applied Energy, Volume 171, 1 June 2016, Pages 357–371

کلمات کلیدی
مدیریت انرژی؛ الگوریتم جستجوی فاخته ؛ تغییر زمان مطلوب؛ منابع انرژی تجدیدپذیر؛ هزینه تغییر زمان انرژی
پیش نمایش مقاله
پیش نمایش مقاله مدیریت انرژی توسط برنامه ریزی مجدد ژنراتور در سیستم قدرت آزادشده احتقانی

چکیده انگلیسی

Optimal energy delivery and energy consumption is vital in electric power systems as large amount of electricity cannot be stored in its electrical form. As part of upgradation, power systems are undergoing deregulation. One among the key issues of the deregulated power system is overload on a transmission line, also referred as congestion. Congestion is not acceptable as it increases the energy price and threatens system reliability and security. In this paper, a method of energy management is presented to remove congestion on transmission lines by rescheduling generators with the objective of minimizing energy rescheduling cost on day-ahead and hour-ahead basis. Usually, optimization methods are useful to achieve maximum gain. The Cuckoo Search Algorithm is employed in this article in order to get the optimized result. Numerical analysis of modified IEEE 30-bus system and real time application for TamilNadu (TN) 106-bus system is presented to provide evidence of the performance of the energy management measure. The realistic cases of base load, peak load, bilateral and multilateral power transactions, generation failure, and transmission line outages are considered and their corresponding energy generation, energy consumption and energy savings are obtained and are compared with the results of Particle Swarm Optimization. The discussed results show that the presented approach of energy management can reduce energy rescheduling cost and energy generation cost. In addition to that, the rescheduling of generators based on Renewable Energy Sources (RES) can further reduce the congestion cost, system energy loss and the usage of fossil fuels. The presented algorithm takes less computational time to achieve their optimal energy rescheduling cost when compared with Particle Swarm Optimization.

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