Impact analysis of renewable energy resources and electric vehicles in hybrid power systems

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-09-26 DOI:10.1016/j.compeleceng.2025.110729

Anil Kumar, Saurabh Chanana, Amit Kumar

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引用次数: 0

Abstract

This study concentrates on improving load frequency control (LFC) methods for integrated power networks, particularly addressing the fluctuating attributes of energy from renewable sources and electric vehicles. A modified fractional order controller (i.e., fractional order integral-proportional derivative with filter (FOI-PDF)) has been built for the system being studied. Additionally, a new optimization named the Electric Eel Foraging Optimisation (EEFO) has been introduced for improving the settings of various controller parameters. The proposed system under analysis is mathematically modelled and examined to include hydro power plants (HPPs), thermal power plants (TPPs), and gas power plants (GPPs) in each of the two interconnected hybrid power systems. Furthermore, to accommodate case studies, both control areas connect intermittent power from wind power plants (WPPs) & solar power plants (SPPs) along with electric vehicles (EVs) and also examine the effect of communication time (CT) delay. The proposed EEFO optimisation technique surpasses earlier meta-heuristic optimization techniques (MOTs) like (Whale Optimisation Algorithm (WOA), Sine Cosine Algorithm (SCA), Quadratic Interpolation Optimisation (QIV), Arithmetic Optimisation Algorithm (AOA), and Ant Lion Optimisation (ALO)) in terms of convergence curve and the objective function of integral time absolute error (ITAE) value. The ITAE value of EEFO is 88.74%, 88.99%, 5.54%, 90.51%, and 90.27% lower than the values of WOA, SCA, QIV, AOA, and ALO, respectively. A thorough evaluation of several scenarios, including step, multistep, and random disturbances, has been carried out to assess the effectiveness of the suggested control method in contrast to current controllers. In the case of step load disturbances (SLDs), the settling time of the EEFO-based FOI-PDF is 46.05% faster than the recently developed fractional order integral derivative-tilt (FID-T) controller in ΔF₁, 19.65% faster in ΔF₂, and 63% faster in ΔP_tie, respectively. The comprehensive data investigations indicate that the anticipated hybrid power system is the subject of a dynamic performance study that is both superior and enhanced. Additionally, the stability study, encompassing Bode plots and eigenvalues along with sensitivity analysis, has been conducted. The proposed methodology has been validated by an empirical inquiry carried out in real real-time simulator using the OPAL-RT platform.

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混合动力系统中可再生能源与电动汽车的影响分析

本研究的重点是改进综合电网的负荷频率控制（LFC）方法，特别是解决可再生能源和电动汽车能源的波动属性。针对所研究的系统，建立了一种改进的分数阶控制器（即带滤波器的分数阶积分-比例导数（FOI-PDF））。此外，还引入了一种新的优化方法，称为电鳗觅食优化（EEFO），用于改进各种控制器参数的设置。所提出的系统分析是数学建模和检查，包括水力发电厂（HPPs），火力发电厂（TPPs），和燃气发电厂（GPPs）在每一个互连的混合动力系统。此外，为了适应案例研究，两个控制区将风力发电厂（WPPs）和太阳能发电厂（SPPs）的间歇性电力与电动汽车（ev）连接起来，并检查通信时间（CT）延迟的影响。提出的EEFO优化技术在收敛曲线和积分时间绝对误差（ITAE）值的目标函数方面，超越了鲸鱼优化算法（WOA）、正弦余弦算法（SCA）、二次插值优化（QIV）、算术优化算法（AOA）和蚂蚁狮子优化（ALO）等早期的元启发式优化技术（MOTs）。EEFO的ITAE值比WOA、SCA、QIV、AOA和ALO分别低88.74%、88.99%、5.54%、90.51%和90.27%。对几种情况进行了全面的评估，包括步进、多步和随机干扰，以评估与当前控制器相比，所建议的控制方法的有效性。在阶跃负载扰动（SLDs）的情况下，基于eefo的FOI-PDF的沉降时间比最近开发的分数阶积分导数-倾斜（fidt）控制器分别快46.05% （ΔF1）、19.65% （ΔF2）和63% （ΔPtie）。综合数据调查表明，预期的混合动力系统是一个既有优势又有增强的动态性能研究对象。此外，还进行了稳定性研究，包括波德图和特征值以及灵敏度分析。所提出的方法已通过使用OPAL-RT平台在实时模拟器中进行的经验调查进行了验证。

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

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.