کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11020784 | 1715614 | 2019 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms
ترجمه فارسی عنوان
ارزیابی قابلیت اطمینان سیستم قدرت مرکزی با مزارع باد و کاربرد آن در ارزیابی اعتبار ظرفیت مزارع باد
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کلمات کلیدی
قابلیت اطمینان سیستم کامپوزیت مزارع بادی، شبیه سازی غیر تدریجی مونت کارلو، ادغام دولت، محاسبات موازی، اعتبار ظرفیت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
This paper presents a non-sequential Monte Carlo Simulation (MCS)-based method for the reliability assessment of composite power system with wind farms (WFs). A multistate probability table and its corresponding Spearman's rank correlation coefficient (SRCC) are combined to represent the power outputs of WFs, which makes the multistate model of WFs compatible with the non-sequential MCS while considering the dependence among power outputs of WFs. By constructing a system state array with encoding conversion, a state merging technique is proposed, which significantly reduces the number of system states to be evaluated. In addition, the parallel computing technique is employed to accelerate the contingency analysis for the merged system states. Furthermore, the capacity credit (CC) of WFs considering both wind power correlation and transmission network constraints is evaluated based on the proposed reliability assessment method. Finally, the effectiveness of the proposed reliability assessment method and its application in the CC evaluation are demonstrated using extensive numerical studies on several modified test systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electric Power Systems Research - Volume 166, January 2019, Pages 73-82
Journal: Electric Power Systems Research - Volume 166, January 2019, Pages 73-82
نویسندگان
Fan Chen, Fangxing Li, Wei Feng, Zhinong Wei, Hantao Cui, Haitao Liu,