Vulnerability analysis in power systems is a key issue in modern society and many efforts have contributed to the analysis. Recently, complex networks metrics, applied to assess the topological vulnerability of networked systems, have been used in power grids, such as the betweenness centrality. These metrics may be useful for analyzing the topological vulnerability of power systems because of a close link between their topological structure and physical behavior. However, a pure topological approach fails to capture the electrical specificity of power grids. For this reason, an extended topological method has been proposed by incorporating several electrical features, such as electrical distance, power transfer distribution, and line flow limits, into the pure topological metrics. Starting from the purely topological concept of complex networks, this paper defines an extended betweenness centrality which considers the characteristics of power grids and can measure the local importance of the elements in power grids. The line extended betweenness is compared with the topological betweenness and with the averaged power flow on each line over various operational states in the Italian power grid. The results show that the extended betweenness is superior to topological betweenness in the identification of critical components in power grids and at the same time could be a complementary tool to efficiently enhance vulnerability analysis based on electrical engineering methods.

Electric infrastructures have been pushed forward to handle tasks they were not originally designed to perform. To improve reliability and efficiency, state-of-the-art power grids include improved security, reduced peak loads, increased integration of renewable sources, and lower operational costs. In this framework“, smart grids” are built around bidirectional communication technologies, where “smart meters” communicate with all other entities and collect data from the power grid, offering specific features to each actor playing in the energy marketplace. In this paper, to overcome some of the challenges raised by smart grids and smart meters, we propose a distributed metering infrastructure which provides bidirectional communication, self-configuration, and auto-update capabilities. Our 3-phase smart meters follow the basics Internet-of-Things principles and have the ability to run, either on-board or distributed on the network, multiple algorithms for smart grid management. These algorithms can be freely added, updated, or removed on-the-fly thanks to the auto-update feature of the system. Moreover, to reduce costs and improve scalability, we prove that it is possible to implement our smart meters using only off-the-shelf and inexpensive hardware devices. A digital real-time simulator (i.e., Opal-RT) has been used to assess the capabilities of both the infrastructure and the meter. Our experimental analysis shows that the latency introduced by the data transmission over the Internet is compliant with the limits imposed by the IEC 61850 standard. As a consequence, our architecture does not affect the operational status of the smart grid, making it a viable solution to support the deployment of novel services.

The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applica- tions are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud

In this paper, we present a novel distributed framework for real time management and co-simulation of Demand Response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real internet-connected smart devices deployed at customer premises and along the Smart Grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world Smart Grid. This is demonstrated on a realistic smart grid with a test case DR-policy.

While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform.