Special Issue "Cyber-physical Systems for Smart Grids"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Tamas Keviczky Website E-Mail
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CD, Delft, The Netherlands
Interests: distributed control and estimation, distributed optimization, model predictive control, distributed robotics, large-scale systems, networked control systems, embedded and real-time optimization based control, thermal/electric power/water-networks and smart grids.

Special Issue Information

Dear Colleagues,

The energy transition—fueled by various non-negotiable societal trends, such as the electrification of all sectors, de-carbonisation, and awareness—has led to the concept of smart grids, which are expected to tackle pressing technological challenges due to the growing share of renewable sources, while leveraging the increasing digitalization of our energy networks. However, as the complexity of man-made engineering systems (such as electricity, heat, gas, and other types of energy networks, industrial processes, transport, and the built environment) rapidly evolve beyond the complete understanding and influence of their creators, new approaches are sought to understand, design, and control these pieces of critical infrastructure as integrated energy systems. Since they comprise both grid technology (generation, transmission, distribution hardware, conversion devices, pipes, storage, and more) and the underlying intelligence (in terms of ICT, algorithms, data, operations, controls, management, balancing, security and quality of supply, analytics, and planning), they are prime examples for what are called large-scale cyber–physical systems.

This Special Issue aims to publish articles that provide novel insights, theories, and solutions for smart grids viewed as cyber–physical systems. The subject areas may range from methods for the analysis of complex energy systems, where advanced mathematics and measurement techniques are used to tackle the complexity of future smart grids stemming from renewable generation, from the management of flexibility and storage, to vehicle-to-grid challenges, and planning and scheduling under increased uncertainty, to name a few.

Prof. Dr. Tamás Keviczky
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  Cyber–Physical Systems
  • Smart Grids
  • Energy Conversion and Storage
  • Power-to-X Concept
  • Electric Vehicle Charging
  • Microgrids
  • Heat-, Power- and Gas-networks
  • Renewables
  • Distribution
  • Digitalization
  • Data Analytics
  • Control Systems
  • Algorithmic Design
  • Optimization, Planning, and Scheduling in Smart Grids

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders
Energies 2019, 12(16), 3091; https://doi.org/10.3390/en12163091 - 11 Aug 2019
Abstract
As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in [...] Read more.
As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks. Full article
(This article belongs to the Special Issue Cyber-physical Systems for Smart Grids)
Show Figures

Graphical abstract

Back to TopTop