مشخصات مقاله | |
ترجمه عنوان مقاله | یک روش تحلیلی مبتنی بر سناریو برای تجزیه و تحلیل جریان احتمالی بار |
عنوان انگلیسی مقاله | A scenario-based analytical method for probabilistic load flow analysis |
انتشار | مقاله سال 2020 |
تعداد صفحات مقاله انگلیسی | 9 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
3.782 در سال 2019 |
شاخص H_index | 104 در سال 2020 |
شاخص SJR | 1.037 در سال 2019 |
شناسه ISSN | 0378-7796 |
شاخص Quartile (چارک) | Q1 در سال 2019 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی برق |
گرایش های مرتبط | مهندسی الکترونیک |
نوع ارائه مقاله |
ژورنال |
مجله | تحقیقات سیستم های توان برق – Electric Power Systems Research |
دانشگاه | School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei Province, China |
کلمات کلیدی | جریان احتمالی بار، منبع انرژی تجدیدپذیر، همبستگی، تجزیه و تحلیل سناریو، انباشته |
کلمات کلیدی انگلیسی | Probabilistic load flow، Renewable energy source، Correlation، Scenario analysis، Cumulant |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.epsr.2019.106193 |
کد محصول | E14495 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract 1. Introduction 2. Modeling of uncertainty 3. Scenario analysis of stochastic variable 4. Scenario-based cumulant method for PLF 5. Case study 6. Conclusion Conflict of interest CRediT authorship contribution statement Acknowledgment References |
بخشی از متن مقاله: |
Abstract
In recent years, power system uncertainties have increased due to the growing integrations of intermittent renewable energy resources. It is imperative to introduce probabilistic load flow analysis in the study of power system operation and planning to adapt to the ever-increasing uncertainties. This paper proposes a scenariobased analytical method for the probabilistic load flow analysis, which takes advantage of both the scenario analysis method and the cumulant method. This method can not only consider various kinds of correlations among power inputs but also accurately represent the probability distributions of desired outputs with a reasonable computational burden. The performance of this method is evaluated on the IEEE 14-bus and 118-bus test systems. The accuracy and efficiency of the proposed method are validated through quantitative and graphical comparisons with Monte-Carlo simulation. Introduction In the past few years, renewable energy sources (RES) have experienced rapid development due to their numerous advantages. More and more uncertainties have been penetrating into the modern power systems, not only from load demands, network topology changes, outages of system components but also from the generations of RES, such as solar and wind power. Besides, due to complex meteorological processes, there are significant spatiotemporal correlations among the RES generation. Hence, assessing the behaviors of power systems with complex uncertainties becomes indispensable. Probabilistic load flow (PLF), firstly proposed in 1974 [1], has become the commonly used tool to analyze the influence of power system uncertainties. There are three mainstream PLF methods: numerical methods, analytical methods, and approximate methods [2]. As the most straightforward numerical method, Monte-Carlo simulation (MCS) firstly represents the uncertainties of input random variables (RVs) with a series of samples and then obtains the probability distributions of output RVs through a large number of deterministic power flow (DLF) calculations. The traditional MCS method with simple random sampling (MCS-SRS) [3] usually requires 104 –106 trials to harvest accurate results. The massive computational burden hinders its applications in large-scale power systems. Hence, serval advanced sampling techniques, such as Latin supercube sampling [4], uniform design sampling [5], and Latin hypercube sampling (LHS) [6,7] are introduced to improve the computational efficiency. Besides, combined MCS and parallel computing [8] provides a promising approach for online PLF analysis. It achieves high accuracy at a low computational burden. |