مشخصات مقاله | |
ترجمه عنوان مقاله | ارزیابی جریان انرژی احتمالی و ممکن ترکیبی برای سیستم های حامل چند انرژی |
عنوان انگلیسی مقاله | Hybrid Possibilistic-Probabilistic Energy Flow Assessment for Multi-Energy Carrier Systems |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 12 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
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) |
4.641 در سال 2018 |
شاخص H_index | 56 در سال 2019 |
شاخص SJR | 0.609 در سال 2018 |
شناسه ISSN | 2169-3536 |
شاخص Quartile (چارک) | Q2 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی انرژی |
گرایش های مرتبط | سیستم های انرژی |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | College of Information Science and Engineering, Northeastern University, Shenyang 110004, China |
کلمات کلیدی | حامل چند انرژی، عدم قطعیت ممکن، عدم قطعیت احتمالی، جریان انرژی نامعلوم |
کلمات کلیدی انگلیسی | Multi-energy carrier, possibilistic uncertainty, probabilistic uncertainty, uncertain energy flow |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2943998 |
کد محصول | E14086 |
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
I. INTRODUCTION II. SYSTEM MODELING III. UNCERTAINTY IN MULTI-ENERGY FLOW PROBLEM IV. HYBRID POSSIBILISTIC-PROBABILISTIC UNCERTAINTY ALGORITHM V. CASE STUDIES VI. CONCLUSION REFERENCES |
بخشی از متن مقاله: |
ABSTRACT
The uncertainty is a pivotal problem in Multi-Energy Carrier (MEC) systems, which leads to the strong demand of reasonable tools to evaluate uncertainties. When both possibilistic and probabilistic uncertainties exist in the real MEC systems, traditional possibilistic or probabilistic methods are no more suitable to be applied. Therefore, this paper proposes a hybrid possibilistic-probabilistic energy flow assessment method to evaluate these uncertainties. Firstly, to build a more precise uncertain model, the probabilistic and possibilistic uncertainties are respectively modeled by considering different uncertainties of sources, networks and loads of MEC systems, and the correlations among wind generation and energy loads. Then, the product t-norms of the extension principle plus α-cut method is firstly implemented in processing fuzzy energy flow, which can reduce overestimation compared with the sole α-cut method. Next, on the basis of Dempster-Shafer evidence theory, the hybrid possibilistic-probabilistic energy flow assessment approach is presented. Finally, two cases are carried out to verify the effectiveness and practicability of the proposed method. INTRODUCTION Currently, with increasingly global energy crisis and intricate interactions among electricity, gas and heat networks, the development of Multi-Energy Carrier (MEC) systems draws extensive attention worldwide. Meanwhile, Renewable Energy Resources (RESs), such as wind power and photovoltaics, predominate in the sustainable transformation of energy systems, which also devotes to establishing complementary utilization of multiple energy carriers [1], [2]. In the numerous investigation about MEC systems, the uncertainty assessment is a critical issue. As there are various uncertainties (e,g., the variability and intermittency of the RESs [3], [4], stochastic fluctuations in energy loads [5]) in MEC systems, a reasonable tool to evaluate the uncertainties is indispensable to quantify and control the operational and planning risks of MEC systems. Deterministic energy flow calculation provides available measures for uncertain energy flow calculation, and it lays the foundation for planning analysis and optimal operation of MEC systems. The steady-state energy flow of electrical, gas and heat network is firstly investigated on the basis of Newton-Raphson technique considering interactions among different networks [6]. Due to the sensitivity of Newton method to initial guesses, a fast decomposing strategy is proposed to solve energy flow in large scale MEC systems [7]. |