Application of long short-term memory networks in virtual power plant data centers

Jun CHEN; Siheng NING

doi:10.11959/j.issn.2096-0271.2023077

您当前的位置：

首页 >

文章列表页 >

Application of long short-term memory networks in virtual power plant data centers

APPLICATION | 更新时间：2024-06-03

- Application of long short-term memory networks in virtual power plant data centers
- Big data research Vol. 9, Issue 6, Pages: 160-173(2023)
- 作者机构：
  
  1. 上海时石能源有限公司，上海 201402
  2. 中国科学院大学应急管理科学与工程学院，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2023077
  CLC： TP399
- Online First：2023-11，
  
  Published：15 November 2023
- 稿件说明：
移动端阅览
Jun CHEN, Siheng NING. Application of long short-term memory networks in virtual power plant data centers[J]. Big data research, 2023, 9(6): 160-173.
DOI：

Jun CHEN, Siheng NING. Application of long short-term memory networks in virtual power plant data centers[J]. Big data research, 2023, 9(6): 160-173. DOI： 10.11959/j.issn.2096-0271.2023077.

摘要

可再生能源发电具有间歇性、随机性和不可控性，为绿色能源的充分利用带来了挑战。虚拟电厂数据中心具有高能耗特性，因此成为可再生能源中间歇性（非调度性）电力的高效吸纳与调控手段。基于此，提出了一种通过时间嵌词编码的长短期记忆（long short-term memory，LSTM）网络对虚拟电厂“源荷”双侧状态进行预测的方法。该方法可实现15分钟级的“电力短缺”主动预警，为容器的暂停和备份创造充分的缓冲时间窗口，结合容器技术实现数据中心的动态能耗管理，从而提升了虚拟电厂数据中心应对电力供需失衡的鲁棒性。这对稳定电网运行、加速绿色清洁能源应用、构建能源生态的服务格局、加速电网数字化转型具有重要的意义。

Abstract

The intermittent

random and uncontrollable power generation characteristics of renewable energy pose challenges for the full utilization of green energy.The high energy consumption feature of the virtual power plant data center makes it an efficient absorption and regulation strategy for the intermittent (non-dispatchable) power in renewable energy.This paper proposes a method to predict the ＆quot;source-load＆quot; dual-state of the virtual power plant using a long short-term memory network that incorporates time-embedded encoding.The results indicate that using the model presented in this paper can achieve proactive alerts for ＆quot;power shortages＆quot; at 15-minute intervals

creating ample buffer time windows for container suspension and backup.Combined with container technology

it realizes dynamic energy consumption management in data centers

thereby enhancing the robustness of the virtual power plant data center against power supply-demand imbalances.This technology is of great significance for stabilizing grid operations

accelerating the application of green clean energy

constructing a service pattern for the energy ecosystem and speeding up the digital transformation of the grid.

关键词

Keywords

references

钱晓栋 , 虞伟君 , 徐展 , 等 . “30·60”战略目标下低碳智慧园区“双碳”路径研究 [J ] . 低碳世界 , 2022 , 12 ( 8 ): 10 - 12 .

QIAN X D , YU W J , XU Z , et al . Research on “double carbon” path of low-carbon smart park under the strategic goal of“30.60” [J ] . Low Carbon World , 2022 , 12 ( 8 ): 10 - 12 .

钟永洁 , 纪陵 , 李靖霞 , 等 . 虚拟电厂基础特征内涵与发展现状概述 [J ] . 综合智慧能源 , 2022 ( 6 ): 25 - 36 .

ZHONG Y J , JI L , LI J X , et al . Overview on the characteristics connotation and development status of virtual power plants in China [J ] . Integrated Intelligent Energy , 2022 ( 6 ): 25 - 36 .

刘伟 . 互联网发展下的电力源网荷储一体化项目建设分析 [J ] . 中国设备工程 , 2023 , 17 : 249 - 251 .

LIU W . Analysis on the construction of integrated project of power source,grid,load and storage under the development of Internet [J ] . China Plant Engineering , 2023 , 17 : 249 - 251 .

王丹 , 杨俊涛 , 曾燕 , 等 . 考虑源荷不确定性的配电网风光储综合规划 [J ] . 电力需求侧管理 , 2023 , 25 ( 5 ): 65 - 72 .

WANG D , YANG J T , ZENG Y , et al . Integrated planning of wind,solar and energy storage in distribution network considering uncertainty of source and load [J ] . Power Demand Side Management , 2023 , 25 ( 5 ): 65 - 72 .

董治新 , 韩雅萱 , 杨丽 , 等 . “源-网-荷储”互动下适应多元主体需求的灵活性交易机制研究 [J ] . 现代电力 ,doi:10.19725/j.cnki.1007-2322.2022.0289.

DONG Z X , HAN Y X , YANG L , et al . Research on flexible trading mechanism catering to requirements of multiple entities under “generationgrid-load-storage” interaction [J ] . Modern Electric Power ,doi:10.19725/j.cnki.1007-2322.2022.0289.

肖佳 , 梅琦 , 黄晓琪 , 等 . “双碳”目标下我国光伏发电技术现状与发展趋势 [J ] . 天然气技术与经济 , 2022 , 16 ( 5 ): 64 - 69 .

XIAO J , MEI Q , HUANG X Q , et al . Status quo and development trend of photovoltaic power-generating technology under the dual-carbon goal [J ] . Natural Gas Technology and Economy , 2022 , 16 ( 5 ): 64 - 69 .

王鑫 , 吴际 , 刘超 , 等 . 基于LSTM循环神经网络的故障时间序列预测 [J ] . 北京航空航天大学学报 , 2018 , 44 ( 4 ): 772 - 784 .

WANG X , WU J , LIU C , et al . Exploring LSTM based recurrent neural network for failure time series prediction [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2018 , 44 ( 4 ): 772 - 784 .

SAK H , SENIOR A , BEAUFAYS F , et al . Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition [EB ] . arXiv preprint , 2014 ,arXiv:1402.1128.

Views

351

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

High resolution remote sensing image object detection algorithm based on improved YOLOv8

A survey of sand-dust image visual enhancement techniques

A survey of emotional speech synthesis

Multiple-feature fusion based generative adversarial network for image dehazing

Carbon emission prediction method of steel plants based on long short-term memory network

Related Author

QIAO Huanyu

CAO Feng

ZHANG Xia

SI Yazhong

ZHANG Xulong

YANG Fan

CHENG Ning

XIAO Jing

Related Institution

School of Computer and Information Technology, Shanxi University

Hebei University of Technology

Ping An Technology (Shenzhen) Co., Ltd.

University of Science and Technology of China

School of Intelligent and Engineering, Shenyang City University

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273110 Visits Today：16

⁰