基于微服务的解决方案与混合通信,用于智能电网环境中的能源管理.
Artur F S Veloso1, José V Reis1, Ricardo A L Rabelo1
1Department of Computing, Federal University of Piauí (UFPI), Teresina 64049-550, PI, Brazil.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了一种基于微服务的能源管理系统,用于智能电网的混合低功率广域网 (LPWAN). 它通过人工智能驱动的需求响应算法实现了显著的峰值需求减少和成本节省.
科学领域:
- 智能电网技术和能源管理系统.
- 物联网 (IoT) 的无线通信网络.
- 人工智能 (AI) 在能源系统中的应用.
背景情况:
- 越来越多的住宅需求变化和分布式能源对智能电网 (SG) 的稳定性构成挑战.
- 集中的管理模式不足以满足当前的SG运营需求.
- 可扩展,数据驱动的架构对于现代的SGS是必不可少的.
研究的目的:
- 为SGS提出一个可扩展,弹性能源管理解决方案.
- 为了提高物联网和需求响应 (DR) 应用程序的数据采集可靠性.
- 开发和评估人工智能驱动的需求响应算法,以减少峰值负载.
主要方法:
- 开发基于微服务的能源管理系统,利用混合低功耗广域网 (LPWAN),整合长距离广域网 (LoRaWAN) 和LoRaMESH.
- 评估智能电表 (SM),数据聚合点 (DAP) 和聚焦器 (CON) 的原型,以确保通信的稳定性.
- 应用聚合和集群技术,从家庭数据中生成负载概况 (LPs),并评估14个负载转移算法.
主要成果:
- 原型实现了高数据包交付率 (>97%),验证了混合LPWAN通信的稳定性.
- 18:00-21:00之间确定了关键的峰值需求,达到每日平均水平的42%以上.
- 基于意图和弹性 (HAAIR) 的拟议混合适应算法表现出卓越的性能:峰值降低1.83%,节省65.40美元的成本,减少60公斤的二氧化碳,0.04舒适性损失指数,9.5的弹性和0.98的可靠性.
结论:
- 混合LPWAN通信和微服务的整合提高了SG连接性和数据可靠性.
- 由人工智能驱动的HAAIR算法有效地减少了峰值需求以及相关成本和排放.
- 这种综合方法为未来的智能电网提供了一个可扩展,弹性和节能的途径.
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