Cloudhive: A Cloud-Based Framework for Smart Grid Co-Simulation, Data, and Communication

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Kenneth B. Kent, Mengbing Zhou, Gabriel Adeyemo, Yang Wang
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引用次数: 0

Abstract

The integration of renewable energy has driven the need for smart grid frameworks that enable efficient co-simulation, data management, and secure communication. This paper introduces CloudHive, a cloud-native framework designed to address these challenges by unifying large-scale power-network co-simulation, real-time data communication, and big data analytics in a single modular architecture. Unlike existing co-simulation tools or data platforms that operate in isolation, CloudHive uniquely enables bidirectional interaction between simulation environments (e.g., OpenDSS for power systems, OMNeT++ for communication networks) and real-world smart grids, supported by message-oriented middleware (RabbitMQ, Apache Kafka) for low-latency data exchange and Kubernetes for dynamic scalability. We evaluate CloudHive's accuracy, scalability, and usability through three representative case studies. The results show that CloudHive achieves high accuracy, performs well in real-world scenarios, and scales efficiently with growing workloads in cloud environments.

Cloudhive:基于云的智能电网协同仿真、数据和通信框架
可再生能源的整合推动了对智能电网框架的需求,这些框架能够实现高效的协同模拟、数据管理和安全通信。本文介绍了CloudHive,这是一个云原生框架,旨在通过在单个模块化架构中统一大规模电力网络联合仿真,实时数据通信和大数据分析来解决这些挑战。与现有的协同仿真工具或数据平台隔离运行不同,CloudHive独特地实现了仿真环境(例如,电力系统的OpenDSS,通信网络的omnet++)和现实世界智能电网之间的双向交互,由面向消息的中间件(RabbitMQ, Apache Kafka)支持低延迟数据交换,Kubernetes支持动态可扩展性。我们通过三个代表性的案例研究来评估CloudHive的准确性、可扩展性和可用性。结果表明,CloudHive实现了高精度,在真实场景中表现良好,并且在云环境中可以有效地扩展不断增长的工作负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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