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Energies
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The Networked Control and Optimization of the Smart Grid
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The Networked Control and Optimization of the Smart Grid

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 398

Special Issue Editor

Prof. Dr. Peter L. Fuhr

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge, TN 37831, USA
Interests: autonomous systems; electric grid enhancements and resilience drones; sensors; communications

Special Issue Information

Dear Colleagues,

While the concept has been seemingly understood for decades, there is an increasing awareness that a utility’s infrastructure does not operate in isolation, but is rather closely coupled; this is especially as the electric grid morphs from a singular structure to a networked design. The interdependencies of these networked infrastructure components/subsystems exhibit spatial, temporal, operational, and organizational characteristics. For example, the tight coupling between a grid’s infrastructure elements can depend on their geography, simultaneously directly affecting or influencing their operations according to location and potentially inducing cascading failures in a wide area.

Specifically, the operation of networked utility systems such as microgrids places additional constraints on traditional SCADA control systems. As the networks increase in size, the complexity of the interaction of multiple technologies may cause unforeseen operations.  This Special Issue, entitled “The Networked Control and Optimization of the Smart Grid” highlights a variety of such intersecting technical areas. 

Prof. Dr. Peter L. Fuhr
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 submissions that pass pre-check are 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 2600 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

  • sensors
  • communications
  • intelligent systems
  • agent-based networks

Published Papers (1 paper)

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Review

20 pages, 1672 KiB  
Review
Leveraging Gaussian Processes in Remote Sensing
by Emma Foley
Energies 2024, 17(16), 3895; https://doi.org/10.3390/en17163895 - 7 Aug 2024
Viewed by 150
Abstract
Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Monitoring the power grid involves diverse sources of data, including those inherent to the power operation (inertia, damping, etc.) and ambient atmospheric weather data. [...] Read more.
Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Monitoring the power grid involves diverse sources of data, including those inherent to the power operation (inertia, damping, etc.) and ambient atmospheric weather data. TheAutonomous Intelligence Measurements and Sensor Systems (AIMS) project at the Oak Ridge National Laboratory is a project to develop a machine-controlled response team capable of autonomous inspection and reporting with the explicit goal of improved grid reliability. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. GPs have been successful in satellite sensing for physical parameter estimation, and the use of drones for remote sensing is becoming increasingly common. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring large amounts of data is common. Alternatively, traditional machine learning methods perform quickly and accurately but lack the generalizability innate to GPs. The main objective of this review is to gather burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications to assess the current state of the art. The contributions of these works show that GP methods achieve superior model performance in satellite and drone applications. However, more research using drone technology is necessary. Furthermore, there is not a clear consensus on which methods are the best for reducing computational complexity. This review paves several routes for further research as part of the AIMS project. Full article
(This article belongs to the Special Issue The Networked Control and Optimization of the Smart Grid)
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