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Sensor Based Smart Grid in Internet of Things Era
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Sensor Based Smart Grid in Internet of Things Era

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 35537

Special Issue Editors

Prof. Dr. Laura Giarre

E-Mail Website
Guest Editor
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via P. Vivarelli, 10, 41125 Modena, Italy
Interests: networked system (wifi networks; sensor networks; and inventory networks); smart grids; identification and learning; assistive technology; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Federica Pascucci

E-Mail Website
Guest Editor
Dipartimento di Ingegneri, Università di Roma Tre, Via Vito Volterra, 79, 00146 Roma, Italy
Interests: mobile robot perception; data fusion and networked robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The smart grid is an innovative energy network able to improve the conventional electrical grid so to be more reliable, cooperative, responsive, and economical. The smart grid can be regarded as an enabling technology to let the existent cities ready for tomorrow needs.

Within its new capabilities, advanced data sensing, communication, and networking technology are playing significant roles in shaping the future smart grid. Exploiting the Internet of Things technologies paradigm, smart grid technology improves two-way communication between utility companies and customers and allows access to near real-time data that can be used to make cost-effective and environmentally friendly decisions. The IoT approach opens new challenges, such as, for example, designing new sensors, collecting and transmitting information, making intelligent decisions, and optimizing loads. A world of connected assets, meters and substations, vehicles, and devices is the key to a future efficient and reliable smart grid. Not only the smart grid needs to be thought of in terms of vertical applications, but cities and citizens of cities need to be empowered as actors in this revolution.

The aim of the present Special Issue is to investigate all the aspects related to this new connected world, in terms of optimization, communication, control, design, and distribution, with a particular emphasis on the use of IoT solutions.

We encourage authors to submit their interdisciplinary contributions in this area.

Prof. Dr. Laura Giarre
Prof. Dr. Federica Pascucci
Guest Editors

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. Sensors 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

  • Smart Grid: optimization and load distribution, overgrid, and pricing
  • IoT: communication, learning, sensing, control, and distributed
  • Sensors for Smart Grid: design, implementation, and data collection
  • Smart City: applications.

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Published Papers (4 papers)

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18 pages, 3566 KiB  
Article
Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
by Daniele Croce, Fabrizio Giuliano, Ilenia Tinnirello and Laura Giarré
Sensors 2020, 20(8), 2249; https://doi.org/10.3390/s20082249 - 16 Apr 2020
Cited by 8 | Viewed by 3339
Abstract
In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power [...] Read more.
In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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18 pages, 817 KiB  
Article
IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings
by Raffaele Carli, Graziana Cavone, Sarah Ben Othman and Mariagrazia Dotoli
Sensors 2020, 20(3), 781; https://doi.org/10.3390/s20030781 - 31 Jan 2020
Cited by 97 | Viewed by 16098
Abstract
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control [...] Read more.
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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17 pages, 863 KiB  
Article
Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
by Sanket Desai, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti and Seungmin Rho
Sensors 2019, 19(23), 5236; https://doi.org/10.3390/s19235236 - 28 Nov 2019
Cited by 23 | Viewed by 3632
Abstract
With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating [...] Read more.
With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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20 pages, 3665 KiB  
Article
A Fog Computing Enabled Virtual Power Plant Model for Delivery of Frequency Restoration Reserve Services
by Claudia Pop, Marcel Antal, Tudor Cioara, Ionut Anghel, Ioan Salomie and Massimo Bertoncini
Sensors 2019, 19(21), 4688; https://doi.org/10.3390/s19214688 - 28 Oct 2019
Cited by 21 | Viewed by 11480
Abstract
Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services [...] Read more.
Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participation forcing the implementation of new cooperative business models at the local level. This paper is proposing a fog computing infrastructure for the local management of energy systems and the creation of coalitions of prosumers able to provide ancillary services to the grid. It features an edge devices layer for energy monitoring of individual prosumers, a fog layer providing Information and Communication Technologies (ICT) techniques for managing local energy systems by implementing cooperative models, and a cloud layer where the service specific technical requirements are defined. On top, a model has been defined allowing the dynamical construction of coalitions of prosumers as Virtual Power Plants at the fog layer for the provisioning of frequency restoration reserve services while considering both the prosumers’ local constraints and the service ones as well as the constituents’ profit maximization. Simulation results show our solution effectiveness in selecting the optimal coalition of prosumers to reliably deliver the service meeting the technical constraints while featuring a low time and computation overhead being feasible to be run closer to the edge. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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