BIBLIOS

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

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

Participantes na publicação
Maria Fernanda Oliveira Santos (Author)
Wilson de Souza Melo (Author)
Alan Oliveira de Sá (Author)
Dep. Informática
LASIGE
Marco Pasetti (Author)
Paolo Ferrari (Author)

Resumo
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.


Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Suporte
Sustainable Energy, Grids and Networks

Identificadores da Publicação
ISSN - 2352-4677

Editora
Elsevier BV


Identificadores do Documento
DOI - https://doi.org/10.1016/j.segan.2024.101490
URL - http://dx.doi.org/10.1016/j.segan.2024.101490

Identificadores de Qualidade
SCIMAGO Q1 (2023) - 1.232 - Control and Systems Engineering
SCIMAGO Q1 (2023) - 1.232 - Electrical and Electronic Engineering
SCIMAGO Q1 (2023) - 1.232 - Energy Engineering and Power Technology
Google Metrics #17 (2024) - 49 - Power Engineering

Keywords
Distributed Energy Resources Photovoltaic system Cyber-physical systems Risk identification NIST Cybersecurity Framework HAZOP