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面向可控负载的自主式混合发电站的能源管理策略。

Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads.

机构信息

Department of Applied Mathematics, Materials Science and Engineering and Electronic Technology, University Rey Juan Carlos, 28933 Madrid, Spain.

Chemical Engineering Department, Faculty of Applied Sciences, University of Liège, 4000 Liège, Belgium .

出版信息

Sensors (Basel). 2022 Jan 4;22(1):357. doi: 10.3390/s22010357.

DOI:10.3390/s22010357
PMID:35009900
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8749563/
Abstract

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads' operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.

摘要

本文提出了一种用于混合独立电站的能源管理策略(EMS),该电站旨在为可控负载供电。该电站由光伏电池板(PV)、风力涡轮机、柴油发电机和电池组组成。一组电源为可控电气负载供电。所提出的 EMS 旨在通过提供所需的电力来确保负载的电力供应。此外,EMS 确保最大限度地利用可再生能源产生的电力,从而最大限度地减少发电机组的使用,并确保电池组在预定的充电状态值下运行,以确保其安全运行。EMS 提供连接电站组件的开关的切换控制,并决定负载的运行。使用马德里莫斯托莱斯(Mostoles,马德里,西班牙)的实测气候数据对系统进行的仿真表明,所提出的 EMS 满足设计目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/5ec8bb26b11f/sensors-22-00357-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/70199729f33f/sensors-22-00357-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/19172da1c9c9/sensors-22-00357-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/154d948433cf/sensors-22-00357-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/c19509a68e48/sensors-22-00357-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/5513643ba252/sensors-22-00357-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/32039e9606b7/sensors-22-00357-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/b351f643dba9/sensors-22-00357-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/1d015300f120/sensors-22-00357-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/5ec8bb26b11f/sensors-22-00357-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/70199729f33f/sensors-22-00357-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/19172da1c9c9/sensors-22-00357-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/154d948433cf/sensors-22-00357-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/c19509a68e48/sensors-22-00357-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/5513643ba252/sensors-22-00357-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/32039e9606b7/sensors-22-00357-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/b351f643dba9/sensors-22-00357-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/1d015300f120/sensors-22-00357-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6df/8749563/5ec8bb26b11f/sensors-22-00357-g009.jpg

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Prediction of I-V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network.基于卷积神经网络的光伏组件 I-V 特性曲线预测。
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