储能科学与技术 ›› 2024, Vol. 13 ›› Issue (5): 1603-1605.doi: 10.19799/j.cnki.2095-4239.2024.0366

• 储能系统与工程 • 上一篇    下一篇

大数据型热储存系统的能量存储安全性

史玲华(), 吴松丽   

  1. 驻马店职业技术学院信息工程学院,河南 驻马店 463000
  • 收稿日期:2024-04-24 修回日期:2024-05-10 出版日期:2024-05-28 发布日期:2024-05-28
  • 通讯作者: 史玲华 E-mail:13723076386@163.com;@163.com
  • 作者简介:史玲华(1982—),女,硕士,副教授,主要研究方向为计算机硬件技术,计算机软件及计算机应用,E-mail:13723076386@163.com
  • 基金资助:
    河南省社会科学界联合会调研课题(SKL-2023-1712)

Energy storage safety of big data type heat storage system

Linghua SHI(), Songli WU   

  1. College of Information and Engineering, Zhumadian Vocational and Technical College, Zhumadian 463000, Henan, China
  • Received:2024-04-24 Revised:2024-05-10 Online:2024-05-28 Published:2024-05-28
  • Contact: Linghua SHI E-mail:13723076386@163.com;@163.com

摘要:

热储存系统可以有效缓解太阳能随机性和波动性带来的发电问题,是目前光热电厂工作运行最重要的系统之一,但因为其在日常应用中需要频繁切换工作状态,很容易出现数据类异常问题。大数据的引入以及大数据型热储存系统的设计是解决其问题的一项重要尝试。本文首先分析了热储存系统模型的最新研究应用理论,并以美国Andasol光热存储系统为例,重点分析了其模型设计构造原理,以及能量平衡的计算方法,最后从网络无关性检验和融合模型数据诊断两方面,进一步阐述了大数据技术在热储存模型系统安全性的重要应用。

关键词: 大数据, 能量安全, 系统, 储能

Abstract:

Thermal storage systems can effectively alleviate the power generation problems caused by the randomness and volatility of solar energy, and are currently one of the most important systems for the operation of photovoltaic power plants. However, due to the frequent switching of working states in daily applications, it is prone to data anomalies. The introduction of big data and the design of big data based thermal storage systems are important attempts to solve their problems. The article first analyzes the latest research and application theories of thermal storage system models, and takes the Andasol solar thermal storage system in the United States as an example to focus on the design and construction principles of its model, as well as the calculation method of energy balance. Finally, from two aspects: network independence testing and fusion model data diagnosis, the important application of big data technology in the security of thermal storage model systems is further elaborated.

Key words: big data, energy safety, system, energy storage

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