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台湾金门岛的分布式光伏、储能装置和充电站定制能源管理系统。

A Customized Energy Management System for Distributed PV, Energy Storage Units, and Charging Stations on Kinmen Island of Taiwan.

机构信息

Department of Computer Science and Information Engineering, National Quemoy University, Kinmen County 892, Taiwan.

Department of Architecture, National Quemoy University, Kinmen County 892, Taiwan.

出版信息

Sensors (Basel). 2023 Jun 2;23(11):5286. doi: 10.3390/s23115286.

Abstract

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and smart grid. With this motivation in mind, the main objective of this study is to design and deploy an energy management system for hundreds of current PV sites distributed on the island, energy storage systems, and charging stations on the island. In addition, the real-time acquisition of the data for power generation, power storage, and power consumption systems will be used for future demand and response analysis. Moreover, the accumulated dataset will also be utilized for the forecast or prediction of renewable energy generated by the PV systems or power consumed by the battery units or charging stations. The results of this study are promising since a practical, robust, and workable system and database are developed and implemented with a variety of Internet of Things (IoT), data transmission technologies, and the hybrid of on-premises and cloud servers. Users of the proposed system can remotely access the visualized data through the user-friendly web-based and Line bot interfaces seamlessly.

摘要

金门,这座著名的冷战岛屿,又名“马祖”,是一个典型的电网孤岛。为了实现低碳岛屿和智能电网的目标,金门岛大力推广可再生能源和电动汽车,并将其视为两项重要策略。基于这一动机,本研究的主要目标是设计和部署一个能源管理系统,用于管理岛上数百个分布式光伏电站、储能系统和充电站。此外,还将实时采集发电、储能和用电系统的数据,用于未来的需求和响应分析。此外,还可以利用积累的数据集对光伏系统产生的可再生能源或电池组或充电站消耗的电量进行预测或预报。研究结果令人鼓舞,因为已经开发和实施了一个实用、健壮和可行的系统和数据库,该系统结合了物联网(IoT)、数据传输技术以及本地服务器和云服务器的混合技术。通过用户友好的基于网络的和 Line bot 接口,用户可以远程访问可视化数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c0a/10256104/72d9bea62fc1/sensors-23-05286-g001.jpg

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