Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 31, No. 3 (March 2018) 405-414    Article in Press

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  DISTRIBUTED GENERATION EXPANSION PLANNING CONSIDERING LOAD GROWTH UNCERTAINTY: A NOVEL MULTI-PERIOD STOCHASTIC MODEL
 
J. Hosseini Molla, T. Barforoushi and J. Adabi Firouzjaee
 
( Received: May 06, 2017 – Accepted in Revised Form: October 12, 2017 )
 
 

Abstract    Abstract – Distributed generation (DG) technology is known as an efficient solution for applying in distribution system planning (DSP) problems. Load growth uncertainty associated with distribution network is a significant source of uncertainty which highly affects optimal management of DGs. In order to handle this problem, a novel model is proposed in this paper based on DG solution, considering load uncertainty. This model is designed to minimize network costs including operation and losses. Genetic algorithm is used with the purpose of finding the optimal places, sizes as well as times for DGs. Load uncertainty is also modeled through Markov tree. To illustrate the effectiveness of the proposed model, it is tested in different scenarios considering the effects of the purchased power price, DG penetration factor and DG operation intervals. These scenarios are conducted in two different phases, with and without uncertainty and the results are then compared and discussed. Moreover, by considering load uncertainty in planning, planning models would be robust against network future load variations.

 

Keywords    Distributed Generation (DG), Distribution System Planning (DSP), Load Growth, Genetic Algorithm, Markov Tree, Uncertainty

 

چکیده    یکی از راهکارهای موثر در برنامه ریزی توسعه سیستمهای توزیع انری الکتریکی، استفاده از تولید پراکنده می باشد. عدم قطعیت در رشد بار شبکه یکی از موضوعات قابل توجه است که می تواند مدیریت بهره برداری و برنامه ریزی توسعه DG ها را تحت تاثیر قرار دهد. به این منظور در این مقاله مدلی تصادفی جدید مبتنی بر راهکار توسعه تولید پراکنده با ملاحظه عدم قطعیت در رشد بار شبکه توزیع ارائه می شود. برای حل مسئله بهینه سازی (تعیین مکان، ظرفیت و زمان نصب مولدها) از الگوریتم ژنتیک استفاده می شود. همچنین عدم قطعیت در رشد بار شبکه توسط درخت مارکوف مدل می شود.برای نشان دادن اثربخشی مدل پیشنهادی سناریوهای متنوعی با ملاحظه اثرات قیمت برق خریداری شده از شبکه بالادست، ضریب نفوذ مولدها و دوره های بهره برداری مولدها مورد مطالعه قرار می گیرد. این سناریوها با ملاحظه عدم قطعیت و بدون آن مطالعه و با یکدیگر مقایسه می شوند. شبیه سازیها نشان می دهند که با ملاحظه عدم قطعیت، مدلهای برنامه ریزی توسعه در برابر تغییرات بار شبکه مقاوم تر خواهند بود.

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