Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Tools for modelling and simulating the Smart Grid

The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by Information and Communication Technologies (ICT) for handling bi-directional energy flows in electric power grids. Enacting control and performance over the massive infrastructure of the SG requires convenient analysis methods. Modelling and simulation (M&S) is a performance evaluation technique used to study virtually any system by testing designs and artificially creating 'what-if' scenarios for system reasoning and advanced analysis. M&S avoids stressing the actual physical infrastructure and systems in production by addressing the problem in a purely computational perspective. Present work compiles a non-exhaustive list of tools for M&S of interest when tackling SG capabilities. Our contribution is to delineate available options for modellers when considering power systems in combination with ICT. We also show the auxiliary tools and details of most relevant solutions pointing out major features and combinations over the years

Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review

The rapid increase of distributed energy resources (DERs) installation at residential and commercial levels can pose significant technical issues on the voltage levels and capacity of the network assets in distribution networks. Most of these issues occur in low-voltage distribution networks (LVDNs) or near customer premises. A lack of understanding of the networks and advanced planning approaches by distribution network providers (DNSPs) has led to rough estimations for maximum DERs penetration levels that LVDNs can accommodate. These issues might under- or over-estimate the actual hosting capacity of the LVDNs. Limited available data on LVDNs' capacity to host DERs makes planning, installing, and connecting new DERs problematic and complex. In addition, the lack of transparency in LVDN data and information leads to model simplifications, such as ignoring the phase imbalance. This can lead to grossly inaccurate results. The main aim of this paper is to enable the understanding of the true extent of local voltage excursions to allow more targeted investment, improve the network's reliability, enhance solar performance distribution, and increase photovoltaic (PV) penetration levels in LVDNs. Therefore, this paper reviews the state-of-the-art best practices in modeling unbalanced LVDNs as accurately as possible to avoid under- or over-estimation of the network's hosting capacity. In addition, several PV system modeling variations are reviewed, showing their limitations and merits as a trade-off between accuracy, computational burden, and data availability. Moreover, the unbalanced power flow representations, solving algorithms, and available tools are explained extensively by providing a comparative study between these tools and the ones most commonly used in Australia. This paper also presents an overview of unbalanced optimal power flow representations with their related objectives, solving algorithms, and tools