Equilibrium Optimizer: Insights, Balance, Diversity for Renewable Energy Resources Based Optimal Power Flow with Multiple Scenarios

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES

Smart Science Pub Date : 2021-05-31 DOI:10.1080/23080477.2021.1932164

Sundaram B. Pandya, H. Jariwala

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引用次数: 6

Abstract

ABSTRACT Today, along with renewable energy sources such as wind generation units and solar photovoltaic systems, the power grid consists of traditional generating units. An approach for solving single-objective optimal power flow problems with the combination of renewable energy resources (RER-OPF) solar and wind power with conventional coal-based power stations is recommended in the proposed paper. In the proposed work, functions of lognormal and Weibull probability distribution are used, respectively, to forecast solar and wind outcomes. The objective feature includes the underestimation service charge and the standby charge for overestimating unusual non-conventional power generation. The quantitative and comparative results show that Equilibrium optimizer (EO) outperforms compare to Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Ions Motion Optimizer (IMO) and Success-History based Adaptive Differential Evolution (SHADE), which are all well-known optimization algorithms for solving RER-OPF problem. The EO optimizer provides the optimum value of each objective function and has merits in solving IEEE-30 bus-based RER-OPF problem, according to several evaluation criteria such as best value statistical criterion. Graphical Abstract

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均衡优化器:基于多场景下可再生能源最优潮流的洞察、平衡、多样性

摘要如今，除了风力发电机组和太阳能光伏系统等可再生能源外，电网由传统发电机组组成。本文提出了一种将可再生能源（RER-OPF）太阳能和风能与传统燃煤发电站相结合来解决单目标最优潮流问题的方法。在所提出的工作中，分别使用对数正态和威布尔概率分布函数来预测太阳能和风能的结果。客观特征包括低估服务费和高估非常规发电的备用费。定量和比较结果表明，均衡优化器（EO）的性能优于Harris Hawks优化器（HHO）、Grey Wolf优化器（GWO）、Ions运动优化器（IMO）和基于成功历史的自适应差分进化算法（SHADE），它们都是解决RER-OPF问题的著名优化算法。EO优化器提供了每个目标函数的最优值，并根据最佳值统计标准等几个评估标准，在解决基于IEEE-30总线的RER-OPF问题方面具有优点。图形摘要

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来源期刊

Smart Science Engineering-Engineering (all)

CiteScore

4.70

自引率

4.30%

发文量

期刊介绍： Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials