Integration of Distributed Generation in Simultaneous Expansion Planning of Subtransmission Lines and Substations
Publish place: 13th Iranian Student Conference on Electrical Engieering
Publish Year: 1389
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,841
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ISCEE13_214
تاریخ نمایه سازی: 14 مرداد 1389
Abstract:
In this paper simultaneous expansion planning of subtransmission lines and substations is implemented. The proposed approach is capable of introducing the candidate substations with regards to distribution network using the modified mathematical clustering algorithm (MMCA). The presence of Distributed Generation (DG), as an alternative for supplying the load of subtransmission system, has been considered in the problem. The presented method simultaneously gives the optimal location and capacity of substations, the optimal allocation of the load points to the substations, optimal subtransmission lines expansion, optimal location and size of DG units and also the optimal power generation of DGs in different levels of the annual Load Duration Curve (LDC). The objectives of the problem and its constraints compose an optimization problem where the Genetic Algorithm (GA) and Linear Programming (LP) are employed to solve it. The effectiveness of the proposed method is demonstrated by its application on a typical subtransmission system, and the results are compared with the expansion planning of the same system without the use of distributed generation.
Keywords:
Simultaneous expansion planning , distributed generation , genetic algorithm , linearprogramming , modified mathematical clustering algorithm , subtransmission system
Authors
Amir Bagheri
Zanjan University
Hesameddin Yousefian
Tafresh University
Morteza Beigli
Zanjan University
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :