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Sensors and Data Analytic Applications for Smart Grid
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Sensors and Data Analytic Applications for Smart Grid

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 20132

Special Issue Editors

Prof. Dr. Carlos León de Mora

E-Mail Website
Guest Editor
Department of Electronic Technology, University of Seville, 41011 Sevilla, Spain
Interests: smart grid; smart cities; artificial intelligence; machine learning; big data analytics; blockchain; cyber physical systems; Edge IA and IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Federico Delfino

E-Mail Website
Guest Editor
Electronics and Telecommunication Engineering and Naval Architecture Department – University of Genoa, Genoa, Italy
Interests: smart grids, sustainable microgrids, demand-side management, integration of renewables & storages into the power delivery system, smart charging strategies for electric mobility, decision support systems for smart city applications
Dr. Enrique Personal

E-Mail Website
Guest Editor
Electronic Technology Department, Escuela Politécnica Superior, University of Seville, St. Virgen de Africa, 7, 41011 Seville, Spain
Interests: fault location; power distribution network; power delivery; underground distribution system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, as a response to the energy demands and commitments of our society, the smart grid paradigm is being adopted by power utilities, pursuing cleaner and more efficient energy. A proof of this is the evolution that the network topology has gone through, in which the deployment of distributed generation, storage, and controllable load systems has grown, resulting  in self-controlled microgrids, virtual power plants and, more generally, smart local energy communities. Furthermore, these new architectures of decentralized power infrastructures require more resilience and faster communication systems that support the demands of this network philosophy. Thus, in this scenario, new approaches in sensor deployments and new algorithms to analyze the information obtained from them are becoming an essential tool to support the network growth. Additionally, due to the wide areas occupied by this type of power grids and the variety of systems involved, the use of communication systems and protocols that allow them to guarantee the interoperability as well as the integrity and coherence of the information is also essential.

In this sense, in order to face the aforementioned challenges, we have proposed this Special Issue, titled “Sensors and Data Analytic Applications for Smart Grid”. Under this title, we expect high-quality unpublished papers focused on the design and use of new sensor architectures for Smart Grid applications, which include but are not limited to the following topics:

  • Advanced Metering Infrastructures
  • Wide-Area Monitoring, Protection, and Control systems
  • Demand-Side Management systems
  • Smart home and smart building sensors and IoT applications
  • Energy monitoring and energy disaggregation
  • Forecasting and energy management
  • Technical and non-technical losses estimation
  • Electric vehicle as a sensor in energy management
  • Energy flexibility market devices
  • Monitoring systems for asset management
  • Cyber-physical systems in energy applications
  • Communication protocols and standard analysis
  • Transitive energy and blockchain applications for data sensor integrity

Contributions can deal with any aspect of system modeling and simulation but can also cover demonstration and experimental activities and living-lab experiences.

Prof. Dr. Carlos León de Mora
Prof. Dr. Federico Delfino
Prof. Dr. Enrique Personal
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 Sensors 
  • Advanced Metering Infrastructures
  • Demand-Side Management
  • Microgrid and Virtual Power Plant management
  • Energy flexibility market
  • Monitoring systems for asset management

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Published Papers (4 papers)

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Research

24 pages, 17397 KiB  
Article
Flexibility Services Based on OpenADR Protocol for DSO Level
by Juan Ignacio Guerrero Alonso, Enrique Personal, Sebastián García, Antonio Parejo, Mansueto Rossi, Antonio García, Federico Delfino, Ricardo Pérez and Carlos León
Sensors 2020, 20(21), 6266; https://doi.org/10.3390/s20216266 - 3 Nov 2020
Cited by 4 | Viewed by 3617
Abstract
Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more [...] Read more.
Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more complex, requiring specific technologies to balance it. In this sense, the demand-side management is one of its techniques; the demand response is a promising approach for providing Flexibility Services (FSs) and complying with the regulatory directives of the energy market. As a solution, this paper proposes the use of the OpenADR (Open Automated Demand Response) standard protocol in combination with a Decentralized Permissioned Market Place (DPMP) based on Blockchain. On one hand, OpenADR hierarchical architecture based on distributed nodes provides communication between stakeholders, adding monitoring and management services. Further, this architecture is compatible with an aggregator schema that guarantees the compliance with the strictest regulatory framework (i.e., European market). On the other hand, DPMP is included at different levels of this architecture, providing a global solution to Flexibility Service Providers (FSP) that can be adapted depending on the regulation of a specific country. As a proof of concept, this paper shows the result of a real experimental case, which implements a Capacity Bidding Program where the OpenADR protocol is used as a communication method to control and monitor energy consumption. In parallel, the proposed DPMP based on Blockchain makes it possible to manage the incentives of FSs, enabling the integration of local and global markets. Full article
(This article belongs to the Special Issue Sensors and Data Analytic Applications for Smart Grid)
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27 pages, 8120 KiB  
Article
The Power of Big Data and Data Analytics for AMI Data: A Case Study
by Jenniffer Sidney Guerrero-Prado, Wilfredo Alfonso-Morales, Eduardo Caicedo-Bravo, Benjamín Zayas-Pérez and Alfredo Espinosa-Reza
Sensors 2020, 20(11), 3289; https://doi.org/10.3390/s20113289 - 9 Jun 2020
Cited by 24 | Viewed by 7684
Abstract
In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage [...] Read more.
In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid. Full article
(This article belongs to the Special Issue Sensors and Data Analytic Applications for Smart Grid)
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25 pages, 14707 KiB  
Article
Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
by Joaquin Luque, Davide Anguita, Francisco Pérez and Robert Denda
Sensors 2020, 20(10), 2912; https://doi.org/10.3390/s20102912 - 21 May 2020
Cited by 19 | Viewed by 5483
Abstract
The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform [...] Read more.
The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques. Full article
(This article belongs to the Special Issue Sensors and Data Analytic Applications for Smart Grid)
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18 pages, 747 KiB  
Article
A Multi-User, Single-Authentication Protocol for Smart Grid Architectures
by Ahmed S. Alfakeeh, Sarmadullah Khan and Ali Hilal Al-Bayatti
Sensors 2020, 20(6), 1581; https://doi.org/10.3390/s20061581 - 12 Mar 2020
Cited by 10 | Viewed by 2706
Abstract
In a smart grid system, the utility server collects data from various smart grid devices. These data play an important role in the energy distribution and balancing between the energy providers and energy consumers. However, these data are prone to tampering attacks by [...] Read more.
In a smart grid system, the utility server collects data from various smart grid devices. These data play an important role in the energy distribution and balancing between the energy providers and energy consumers. However, these data are prone to tampering attacks by an attacker, while traversing from the smart grid devices to the utility servers, which may result in energy disruption or imbalance. Thus, an authentication is mandatory to efficiently authenticate the devices and the utility servers and avoid tampering attacks. To this end, a group authentication algorithm is proposed for preserving demand–response security in a smart grid. The proposed mechanism also provides a fine-grained access control feature where the utility server can only access a limited number of smart grid devices. The initial authentication between the utility server and smart grid device in a group involves a single public key operation, while the subsequent authentications with the same device or other devices in the same group do not need a public key operation. This reduces the overall computation and communication overheads and takes less time to successfully establish a secret session key, which is used to exchange sensitive information over an unsecured wireless channel. The resilience of the proposed algorithm is tested against various attacks using formal and informal security analysis. Full article
(This article belongs to the Special Issue Sensors and Data Analytic Applications for Smart Grid)
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