Addressing ORPD problem in a standard IEEE power network accompanied with RESs and FACTs appliances by COMMKE under volatile load scenarios

Q3 Mathematics
Susanta Dutta , Tushnik Sarkar , Chandan Paul , Sabbir Reza Tarafdar , Provas Kumar Roy , Ghanshyam G. Tejani , Seyed Jalaleddin Mousavirad
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引用次数: 0

Abstract

This research examines the optimal reactive power dispatch (ORPD) problem across IEEE 30 & 118 bus experimental networks. In particular, we incorporate renewable energy sources (RESs) like solar photovoltaic (PV) and wind power (WP) into the conventional network after first balancing it. Both singular and multiple objective functions (OFs) are considered here. These are, both alone and together, a drop in aggregated voltage deviation (AVD) over buses and a reduction in active power loss (APL). Twenty one cases in all have been looked at using three test frameworks. TSC-TCR (FACTs devices) with test setup are being used for cases 4–6, 9–12 & 16–21. The objectives have been achieved by the use of the COMMKE algorithm, a multi-trial vector-based monkey king evolution (MMKE) method integrated with oppositional based learning (OBL) and chaotic based learning (CBL). Comparative analysis has also been done on the performance of the other optimization methods that were showcased in the latest ORPD research. Both constant and dynamic load demand scenarios are covered in the study. Appropriate probability density functions (PDF) are used to forecast the uncertain WP, PV source, and load demand. Uncertain situations with fluctuating load demand, wind speed (WS), and sun irradiation (SI) are simulated using Monte Carlo simulations (MCS). The investigations’ findings demonstrate that, in a variety of cases, the COMMKE outperforms optimization techniques found in the recent ORPD literature. The improvement of power network efficiency in ORPD difficulties by the application of TSC-TCR is another noteworthy conclusion. To scrutinize the performance of COMMKE, the identical experiments have been conducted using MMKE & driving training based optimization (DTB) and the results coming from COMMKE, MMKE & DTBO are compared. To make this comparison more lucid, statistical records are produced, box plots are presented, error bar plots are used and moreover one way ANOVA test has been performed over the results generated through the different optimization approaches.
comke在不稳定负载情况下解决带有RESs和FACTs设备的标准IEEE电网中的ORPD问题
本研究探讨IEEE 30 &;最优无功功率调度(ORPD)问题。118总线实验网络。特别是,我们将太阳能光伏(PV)和风能(WP)等可再生能源(RESs)纳入常规电网后,首先进行平衡。这里考虑了单目标函数和多目标函数。无论是单独还是一起,都可以降低总线上的聚合电压偏差(AVD),并降低有功功率损耗(APL)。总共有21个案例使用了三个测试框架。带有测试设置的TSC-TCR (FACTs器件)用于案例4 - 6,9 - 12 &;16日。该目标通过使用COMMKE算法实现,这是一种基于多试验向量的猴王进化(MMKE)方法,结合了基于对立学习(OBL)和基于混沌学习(CBL)。并对最新ORPD研究中所展示的其他优化方法的性能进行了对比分析。本研究涵盖了恒载和动载两种需求情况。采用适当的概率密度函数(PDF)来预测不确定的WP、PV源和负荷需求。采用蒙特卡罗模拟(MCS)方法对负荷需求、风速(WS)和太阳辐照(SI)波动的不确定情况进行了模拟。调查结果表明,在各种情况下,COMMKE优于最近ORPD文献中发现的优化技术。应用TSC-TCR提高ORPD困难地区电网效率是另一个值得注意的结论。为了检验COMMKE的性能,使用MMKE进行了相同的实验;基于驾驶训练的优化(DTB)以及来自COMMKE, MMKE和amp;对dbo进行比较。为了使这种比较更加清晰,产生了统计记录,呈现了箱形图,使用了误差柱图,并且对通过不同优化方法生成的结果进行了单向方差分析检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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