Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach

Parvez, Imtiaz; Sarwat, Arif I.; Wei, Longfei; Sundararajan, Aditya

doi:10.3390/en9090691

Cited by 48 publications

(24 citation statements)

References 32 publications

(26 reference statements)

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“…A business model tool for smart cities to facilitate undertaking strategic decision-making promptly was proposed in [35]. In [36], a localisation-based key management system for meter data encryption in smart cities was proposed. The utilisation of smart parking lots in smart cities was investigated in [37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equation (33) is the usual convex combination requirement in piecewise linear approximation. Two auxiliary continuous variables, ξ ijh and η ije , are defined and these are embedded within Equations (34)- (36), so that at most, four adjacent variables of ψ ijhe can be nonzero. The combination of Equations (34) and (36) specifies that two adjacent ψ ijhe at most in the h ∈ H direction can be nonzero, while Equations (35) and (36) state that at most two adjacent ψ ijhe at most in the ∀e ∈ E can be nonzero.…”

Section: Of 23mentioning

confidence: 99%

“…Two auxiliary continuous variables, ξ ijh and η ije , are defined and these are embedded within Equations (34)- (36), so that at most, four adjacent variables of ψ ijhe can be nonzero. The combination of Equations (34) and (36) specifies that two adjacent ψ ijhe at most in the h ∈ H direction can be nonzero, while Equations (35) and (36) state that at most two adjacent ψ ijhe at most in the ∀e ∈ E can be nonzero. In particular, Equation (36) states that the variables, ξ ijh , η ije , are of a special ordered set (SOS) of Type 2 (i.e., SOS2), where a maximum of two of the latter variables that are adjacent can be nonzero.…”

Section: Of 23mentioning

confidence: 99%

“…The combination of Equations (34) and (36) specifies that two adjacent ψ ijhe at most in the h ∈ H direction can be nonzero, while Equations (35) and (36) state that at most two adjacent ψ ijhe at most in the ∀e ∈ E can be nonzero. In particular, Equation (36) states that the variables, ξ ijh , η ije , are of a special ordered set (SOS) of Type 2 (i.e., SOS2), where a maximum of two of the latter variables that are adjacent can be nonzero. This becomes obvious from Figure 5, since for the grid structure shown and in accordance with the latter equations enforced, not more than two adjacent variables of ξ ijh and η ije can be nonzero in the x and y directions, respectively.…”

Section: Of 23mentioning

confidence: 99%

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Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

et al. 2019

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Many cities around the world are facing immense pressure due to the expediting growth rates in urban population levels. The notion of 'smart cities' has been proposed as a solution to enhance the sustainability of cities through effective urban management of governance, energy and transportation. The research presented herein examines the applicability of a mathematical framework to enhance the sustainability of decisions involved in zoning, land-use allocation and facility location within smart cities. In particular, a mathematical optimisation framework is proposed, which links through with other platforms in city settings, for optimising the zoning, land-use allocation, location of new buildings and the investment decisions made regarding infrastructure works in smart cities. Multiple objective functions are formulated to optimise social, economic and environmental considerations in the urban space. The impact on underlying traffic of location choices made for the newly introduced buildings is accounted for through optimised assignment of traffic to the underlying network. A case example on urban planning and infrastructure development within a smart city is used to demonstrate the applicability of the proposed method.Energies 2019, 12, 1318 2 of 23 increasingly reflected in the operations of existing cities, resulting in an interrelated platform between large numbers of citizens, transport networks, services and urban assets and facilities [9].Decision-making in planning and operations of smart cities needs to be structured around two main considerations, namely strategic and tactical decisions [10]. Yet, current emphasis in the literature is on the technical interfaces making up the various data-exchange enabling platforms placed within the cities, with little emphasis on the strategic and tactical urban planning aspects of smart cities. Within the area of strategic decision-making in city planning, zoning of the city and the location of its operating facilities, including schools, hospitals and so forth, are both of significant relevance [11]. An appropriate selection of zone clusters and the subsequent selection of locations to place buildings in form the main components that are involved in city design. In cities, the positioning of new buildings leads to the generation of traffic demand in the existing network structure [12]. This causes additional traffic loadings on the existing network, and if not well planned for, can result in major transportation delays to network users. Traffic congestion is thought to result in over $121 billion in losses [13] and can increase the amount of carbon emissions from traffic by more than 53% [14]. Another important strategic consideration is the development of the underlying travel network of the region. Specifically, the development of the transport network will be based on the capacities required to handle the initiated travel on the transport networks, while the operations of the network will in turn be associated with the established capacity of the network, along...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Of 23mentioning

confidence: 99%

Section: Of 23mentioning

confidence: 99%

Section: Of 23mentioning

confidence: 99%

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Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

et al. 2019

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“…Security aspects related to smart meters deployment have been analysed from different perspectives, e.g., by the authors of [4,5].…”

Section: Introductionmentioning

confidence: 99%

Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies

et al. 2017

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Abstract:The latest technological developments are challenging for finding new solutions to mitigate the massive integration of renewable-based electricity generation in the electrical networks and to support new and dynamic energy and ancillary services markets. Smart meters have become ubiquitous equipment in the low voltage grid, enabled by the decision made in many countries to support massive deployments. The smart meter is the only equipment mandatory to be mounted when supplying a grid connected user, as it primarily has the function to measure delivered and/or produced energy on its common coupling point with the network, as technical and legal support for billing. Active distribution networks need new functionalities, to cope with the bidirectional energy flow behaviour of the grid, and many smart grid requirements need to be implemented in the near future. However there is no real coupling between smart metering systems and smart grids, as there is not yet a synergy using the opportunity of the high deployment level in smart metering. The paper presents a new approach for managing the smart metering and smart grid orchestration by presenting a new general design based on an unbundled smart meter (USM) concept, labelled as next generation open real-time smart meters (NORM), for integrating the smart meter, phasor measurement unit (PMU) and cyber-security through an enhanced smart metering gateway (SMG). NORM is intended to be deployed everywhere at the prosumer's interface to the grid, as it is usually now done with the standard meter. Furthermore, rich data acquired from NORM is used to demonstrate the potential of assessing grid data inconsistencies at a higher level, as function to be deployed in distribution security monitoring centers, to address the higher level cyber-security threats, such as false data injections and to allow secure grid operations and complex market activities at the same time. The measures are considering only non-sensitive data from a privacy perspective, and is therefore able to be applied everywhere in the grid, down to the end-customer level, where a citizen's personal data protection is an important aspect.

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Data Falsification Detection in AMI: A Secure Perspective Analysis

Vineeth¹,

Sophia²

2022

Artificial Intelligence for Renewable Energy Systems

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Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach

Cited by 48 publications

References 32 publications

Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies

Data Falsification Detection in AMI: A Secure Perspective Analysis

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