برنامه ریزی کوتاه مدت بهینه تولید پراکنده
ترجمه نشده

برنامه ریزی کوتاه مدت بهینه تولید پراکنده

عنوان فارسی مقاله: برنامه ریزی کوتاه مدت بهینه چند منظوره تولید پراکنده تجدید پذیر و بانک های خازنی در سیستم توان با توجه به عدم قطعیت های مختلف از جمله وسایل نقلیه الکتریکی پلاگین
عنوان انگلیسی مقاله: Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles
مجله/کنفرانس: مجله بین المللی سیستم های برق و انرژی – International Journal of Electrical Power & Energy Systems
رشته های تحصیلی مرتبط: مهندسی برق
گرایش های تحصیلی مرتبط: تولید انتقال و توزیع، ماشین های الکتریکی، برق قدرت
کلمات کلیدی فارسی: تولید پراکنده تجدیدپذیر، روش تخمین نقطه ای، اندازه بندی و استقرار، بهینه سازی چند منظوره، برنامه ریزی محدود فرصت
کلمات کلیدی انگلیسی: Renewable distributed generation، Point estimate method، Sizing and siting، Multi-objective optimization، Chance constrained programming
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ijepes.2020.105885
دانشگاه: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
صفحات مقاله انگلیسی: 13
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 5.627 در سال 2019
شاخص H_index: 100 در سال 2020
شاخص SJR: 1.260 در سال 2019
شناسه ISSN: 0142-0615
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14498
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

Graphical abstract

Nomenclature

۱٫ Introduction

۲٫ Formulation

۳٫ Methodology

۴٫ Results

۵٫ Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Appendix A. Supplementary data

Research Data

References

بخشی از مقاله (انگلیسی)

Abstract

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

Introduction

The leading motivations in integrating distributed generations (DGs) in power systems are loss reduction, increasing reliability and voltage profile improvement [1]. However, the well-known defects of conventional DGs and the evident improvement in the competitiveness of renewable energy sources (RES) in terms of capital cost are encouraging investors to replace conventional DGs with solar distributed generations (SDGs) and wind distributed generations (WDGs) [2,3]. Optimal integration of these renewable DGs in power systems is crucial for their safe and economical operation [4]. Natural intermittencies of solar irradiation, wind speed, and plug-in electric vehicles (PEVs) load demand as a new aspect of power system should also be assimilated into sizing and siting problems, which is a mixed-integer nonlinear problem subjected to multiple objectives and constraints and many local optimums [5]. Additionally, the problem should handle these uncertainties with a reasonable tradeoff between computational burden and accuracy. Moreover, it might be in favor of the system operator to ignore the small probabilities of violation for soft constraints such as voltage and power limits [6]. According to the literature, DG planning can be generally categorized into single objective and multi-objective formulation [7]. They can also be categorized into subcategories such as deterministic, probabilistic, or in terms of the algorithm they apply, such as metaheuristic, analytical, etc. [5]. In the field of multi-objective DG allocation and sizing problems, some significant contributions have been made by [8–13].