The Networked Control and Optimization of the Smart Grid

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.

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

Title: Comparison of Classification Approaches for Terrestrial and Airborne Mounted Multiband Video Systems for Networked Grid Situations
Authors: Elizabeth Piersall
Affiliation: Oak Ridge National Laboratory
Abstract: Drone based sensors offer unique opportunities for data collection, but also often must operate in noisy, non-ideal conditions, and place weight and power limitations on the sensors used. Cameras are a good general purpose sensor for drones, but in the case of electrical arcs in a networked grid system, a visual camera may not be able to collect as much information as is available. We evaluate arc detection from a combination of ultraviolet and visible data, collected in noisy conditions with low-cost cameras, and the capability of machine learning classification to identify the presence of electrical arcs in varying grid operational and ambient conditions.

Title: Leveraging Gaussian Processes in Remote Sensing
Authors: Emma Foley
Affiliation: University of Tennesse
Abstract: Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance of a networked system’s activities are expensive and sometimes dangerous. Remote sensing enables real time data monitoring and collection related to environmental and industrial processes, like the power grid system. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring a significant amount of data is common. Alternatively, traditional machine learning methods perform quickly and accurately, but lack the generalizability innate to GPs. The focus of this review is burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications.