A hybrid cyber–physical risk identification method for grid-connected photovoltaic systems

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-07-29 DOI:10.1016/j.segan.2024.101490
Maria Fernanda Oliveira Santos , Wilson de Souza Melo Jr. , Alan Oliveira de Sá , Marco Pasetti , Paolo Ferrari
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Abstract

Identifying risks in modern electric power systems is essential, and one of the main difficulties concerns covering the wide range of technologies that permeate its cyber and physical domains. Different risk identification methods have been proposed, but applying them individually does not guarantee coverage of both domains. On the other hand, the simple non-articulated application of a set of existing risk identification methods can lead to an exhaustive and inefficient process. This paper proposes a new Cyber–Physical Risks Identification Method (CPRIM) to comprehensively and efficiently identify risks in electrical power systems. To systematically cover risks ranging from the cyber domain to the physical domain, CPRIM combines in a complimentary and articulated way the National Institute of Standards and Technology (NIST) Cybersecurity Framework, a Risk Factor model, and the HAZOP, establishing a novel hybrid risk identification approach. This work also proposes a method based on Jaccard and overlap indexes to quantitatively assess the complementarity and superposition that may exist when applying different risk identification methods to electrical power systems. The results obtained in a real computer-managed photovoltaic plant indicate that CPRIM can efficiently identify cyber–physical risks, showing a reasonable trade-off between system coverage and redundancy in identified risks.

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并网光伏系统的混合网络物理风险识别方法
识别现代电力系统中的风险至关重要,主要困难之一是要涵盖渗透到网络和物理领域的各种技术。目前已经提出了不同的风险识别方法,但单独应用这些方法并不能保证同时覆盖两个领域。另一方面,简单而不明确地应用一套现有的风险识别方法,可能会导致过程枯燥而低效。本文提出了一种新的网络物理风险识别方法(CPRIM),以全面、高效地识别电力系统中的风险。为了系统地涵盖从网络领域到物理领域的风险,CPRIM 以一种互补和衔接的方式结合了美国国家标准与技术研究院 (NIST) 的网络安全框架、风险因素模型和 HAZOP,建立了一种新型的混合风险识别方法。这项工作还提出了一种基于 Jaccard 和重叠指数的方法,用于定量评估将不同风险识别方法应用于电力系统时可能存在的互补性和叠加性。在实际计算机管理的光伏电站中获得的结果表明,CPRIM 可有效识别网络物理风险,在系统覆盖率和已识别风险的冗余度之间显示出合理的权衡。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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