利用基于 GRU 控制器的能源管理系统降低多功能微电网的成本消耗并管理电力流

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-07-29 DOI:10.1007/s00202-024-02605-3
Harini Vaikund, S. G. Srivani
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引用次数: 0

摘要

全球对能源的需求正在迅速增长,而这种需求大部分是通过生产传统化石燃料来满足的。将可再生能源和混合能源并入电网的最初想法被称为微电网模式。为了在微电网中的每个组件之间进行适当的功率共享,以确保高效、可靠和经济高效的运行,能源管理系统(EMS)通过多种能源资源和存储系统在微电网中发挥着至关重要的作用。能源管理系统中存在的问题是,在适当的时间对能源进行不恰当的预测。这种效率问题会在负荷侧造成一系列与电力相关的问题,并提高电力成本。为了减轻这种影响,我们提出了一种基于深度学习控制器的新型 EMS,以管理所有时段的电力流,降低终端用户的成本。微电网总电力成本和年排放总量的最小化被视为所提模型的首要目标。微电网的设计包括光伏、潮汐、电网和电池,在需求侧考虑了医院和家庭用户。根据负载激活功率需求及其相应的源功率成本,开发了一个实际数据集。利用该数据集设计了深度学习控制器,并通过 coati 优化算法进一步提高了其性能。设计的控制器被安装在 EMS 中,以便在适当的负载需求时段选择适当的电源。在电网链接和电网不喜欢的运行模式下,观察了所提模型的工作状态。所提出的深度学习控制器具有 99.7% 的准确率和 99.5% 的精确度,其结果与其他几种现有方法进行了比较。结果表明,深度学习 EMS 方法能够与多种电源互动,并以合理的成本提供有效的电源管理。
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Mitigation of cost consumption and manage power flows in multi-purpose microgrid using GRU controller-based energy management system

The demand for energy on the global is rising quickly, and the majority of that demand is met by the production of traditional fossil fuels. An original idea for incorporating renewable and hybrid energy sources to a grid was known as microgrid model. For proper power sharing between each component in the microgrid to ensure efficient, dependable, and cost-effective operation, Energy Management Systems (EMS) were crucial in microgrids through multiple energy resources and storage systems. Improper source prediction at the appropriate period was the issue that occurred in the EMS. This problem with efficiency causes a number of power-related issues on the load side and raises electricity costs. To mitigate this impacts, a novel deep learning controller-based EMS was proposed to manage the power flows at all period and reduce the cost of end users. Minimization of microgrid total electricity cost and total annual emission were considered as the primary objectives of the proposed model. Microgrid was designed with PV, tidal, grid, and battery, and in the demand side both hospital and home usages were considered. An actual dataset was developed according to the load activation power demand with its corresponding source power cost. Using this dataset, the deep learning controller was designed, and its performance was further improved through the coati optimization algorithm. The designed controller was fit in the EMS to select the proper source at the appropriate load demand period. The working states of the proposed model were observed under grid linked, and grid disliked mode of operation. The proposed deep learning controller offers 99.7% accuracy and 99.5% precision, and the results were compared to several other existing approaches. The outcomes demonstrate that the deep learning EMS approach was capable of interacting with many power sources and offer effective power management at a reasonable cost.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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