基于EV聚合器的多区域可再生能源电网的先进频率稳定框架:多阶段控制视角

IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Muhammad Inshal Shahzad , Muhammad Majid Gulzar , Salman Habib , Md Shafiullah , Aqsa Shahzad , Muhammad Khalid
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引用次数: 0

摘要

在现代世界,可再生能源的激增已经成为一个焦点,吸引了全球的关注,因为它能够将成本效益与可持续性相结合。这种转变使可再生能源成为我们电网中不可避免的组成部分。一种新的方法被提出用于多区域电力系统的负荷频率控制(LFC),整合不同的能源,如光伏(PV)、电动汽车、风力涡轮机和热电厂。本研究通过研究多级控制器之间的相互通信,深入研究了控制系统的复杂领域,特别是比较了带滤波器PI(2自由度pdn -PI)的2DOF比例积分和导数模型与经典PI和2DOF- pidn控制器。这里的关键区别在于引入了一种增强的coyote优化算法(ECOA),旨在确定这些高级控制器的最佳参数。这项研究的一个独特之处在于它包含了不确定性,通过允许系统参数在± 40 %的范围内波动来解决可变性。在动态负载变化下测试了所建议控制器的鲁棒性,这些变化在多个区域独立应用。采用了两个不同的测试场景,每个场景都受到不同的干扰,以衡量控制器的适应性。采用适当的动态模型,模拟了调速器死区(GDB)对再热调速器机组影响的运行限制和再热发电机组的发电速率约束(GRC)。本研究包括调速器后的GDB和再热机组后的GRC,研究了非线性对电力系统的影响。在控制器之前添加CTD,因为在实际场景中,与系统的通信存在时间延迟。因此,所提出的控制器有助于给出接近实时场景的结果。研究结果表明,在不确定的情况下,结合GRC、GDB和CTD可以成功地抑制振荡,甚至上升。稳定性分析还通过与先前建立的方法进行比较来执行所提出的技术。仿真结果表明,经ECOA优化后的多级2DOF pdn - pi控制系统具有显著的效率和弹性。例如,在系统存在扰动的情况下,所提出控制器的累积沉降时间为1.137 sec,而GA-PI、PSO-PID、ABC-PIDn、COA-PI、COA-PIDn、MPA-PIDn的累积沉降时间分别为28.972 sec、26.42 sec、24.52 sec、17.68 sec、15.125 sec和14.01 sec。其管理跨多区域电力系统的负载频率控制的能力使其与众不同,为现代能源管理的复杂性提供了复杂的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced frequency stabilization framework for multi-area renewable energy grids with EV aggregator support: A multi-stage control perspective
In the modern world, the surge of renewable energy has become a focal point, drawing global attention due to its ability to merge cost-effectiveness with sustainability. This shift has made renewable energy an inescapable component of our power grids. A fresh approach is being proposed for load frequency control (LFC) in multi-area power systems, integrating diverse energy sources like photovoltaic (PV), electric vehicles, wind turbines, and thermal plants. This study dives deep into the complex domain of control systems by examining the intercommunication between multi-stage controllers, specifically comparing the 2DOF proportional integral and derivative with filter-PI (2 Degrees of Freedom PIDn-PI) models against the classic PI and 2DOF-PIDn controllers. The key differentiator here lies in introducing an enhanced coyote optimization algorithm (ECOA), aimed at determining the optimal parameters for these advanced controllers. A unique facet of this research is its inclusion of uncertainty, addressing variability by allowing the system parameters to fluctuate within a range of ± 40 %. The robustness of the suggested controllers is tested under dynamic load changes, with these variations applied independently across multiple regions. Two distinct test scenarios are employed, each subject to varying disturbances, to gauge the controllers' adaptability. The operational restrictions of the governor dead band (GDB) impact on the reheat thermal governor unit and generation rate constraint (GRC) in the reheat thermal generating units are simulated using the proper dynamic models. This research includes GDB after the governor unit and GRC after re-heat unit and studies the effect of nonlinearity in power system. CTD is added before the controller and because, in a realistic scenario, there is a time delay in communication with the system. So, the proposed controller helps to give the results as close as the real-time scenario. The findings reveal that by incorporating the GRC, GDB and CTD, the oscillations are damp successfully and even rise under uncertainty situations. The stability analysis also performs the proposed technique upon comparison with previously established methods. The simulated results imply that the proposed multi-staged 2DOF PIDn-PI control system, optimized by ECOA, exhibits remarkable efficiency and resilience. For instance, in case of perturbation in the system, the cumulative settling time of the proposed controller is 1.137 sec while compared with GA-PI, PSO-PID, ABC-PIDn, COA-PI, COA-PIDn, MPA-PIDn has settling time of 28.972 sec, 26.42 sec, 24.52 sec, 17.68 sec, 15.125 sec and 14.01 sec respectively. Its ability to manage load frequency control across multi-area power systems sets it apart, offering a sophisticated solution to the complexities of modern energy management.
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来源期刊
Sustainable Computing-Informatics & Systems
Sustainable Computing-Informatics & Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTUREC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
10.70
自引率
4.40%
发文量
142
期刊介绍: Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.
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