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Smart Sensor Networks for Smart Grids

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 22318

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Special Issue Editors

Prof. Dr. Panagiotis Papageorgas

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Guest Editor

Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: embedded systems; wireless sensor networks with IoT applications in smart grids; smart cities; internet of energy; LPWAN technologies; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Georgios Vokas

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Smart Technologies, Renewable Energy Sources and Quality Laboratory, Department of Electrical and Electronics Engineering, University of West Attica, GR-122 44, Athens, Greece
Interests: energy yield optimization of solar and wind parks; testing and control of grid-connected commercial inverters; power electronics applications; electric vehicles operation strategies for smart grids; energy strategies and environmental issues using renewable energy sources; economic scenarios for energy planning using RES in normal and smart grids
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Prof. Dr. Constantinos S. Psomopoulos

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Guest Editor

High Voltage and Energy Systems Research Lab, Department of Electrical and Electronics Engineering, University of West Attica, 12244 Egaleo, Greece
Interests: eco design and energy efficiency; materials and energy recovery from wastes; high-voltage engineering; electrical measurements and high field effects; electromechanical installations and apparatus
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Prof. Dr. Radu A. Munteanu

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Guest Editor

Electrical Measurements and Sensors Group, Department of Electrical Engineering and Measurements, Technical University Cluj-Napoca, 28 Memorandumului Street, Cluj-Napoca 400114, Romania
Interests: electrical and electronic measurements; sensors; RAMS (reliability availability maintainability and safety); home automation; measurement signal and biosignal processing; medical engineering; intellectual and industrial property
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Dr. Kyriakos Agavanakis

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Guest Editor

Smart Technologies, Renewable Energy Sources and Quality Laboratory, Department of Electrical and Electronics Engineering, University of West Attica, GR-122 44, Athens, Greece
Interests: embedded systems; smart grid; IoT, Internet of Energy; middleware; cloud computing; big data; machine learning; hw/sw co-design; knowledge sharing; DaaS; PaaS; SaaS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today most of the traditional energy systems are centralized with a large number of customers located within a wide area. Energy is supplied by large power plants that operate according to a centralized coordination mechanism while the integration of distributed renewable energy sources is becoming challenging.

Cyber-physical systems like smart grids are becoming increasingly difficult to be monitored and managed with legacy centralized architectures. The emerging smart grid is implementing a new concept of transmission network which can efficiently route the energy produced from either central and distributed plants to the final user with high security and quality of supply standards. The “internet of energy” concept is defined as a network infrastructure based on standard and interoperable communication transceivers, gateways, and protocols that will allow a real-time balance between the local and the global generation and storage capability with the energy demand. The internet of energy (IoE) concept is based on decentralized energy systems (traditional or renewable) that will be implemented in the opposite way, resembling the evolution of communication networks through the internet. Small-scale energy generators will exist in close proximity to energy consumers (residential or industrial. The introduction of distributed energy sources (DES) and renewable energy sources (RES) has changed the electric grid very rapidly due to the distributed and intermitted nature that characterizes them and the need for a different distribution, transmission, and business model that must be applied in the design of the new smart grid. Although not all the DES and RES are controllable, they can all be enhanced through ICT technology to manage their exhibited duality as cyber-physical system (CPS) nodes. Considered components include the distribution grid infrastructure, switches (manual, autonomous, coordinated), dynamic demand/response scenarios, smart sensors, and actuators, as well as multiple communication technologies (LPWAN, power line, 5G). As a result, efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation are accomplished.

A key concept in the design of the smart grid of tomorrow is the introduction of smart sensors everywhere, from energy generation to the energy consumption utilizing contemporary IoT communication technologies to provide the sensor data securely and in real-time. The deployment of such a sensor network will be the enablement for the transformation of the smart grid and the traditional electric grids to the energy of internet. This transition will be similar to the evolution of the internet achieving the integration of distributed renewable energy sources, the support of the massive deployment of the vehicle to grid (V2G) energy storage infrastructure and optimum utilization of the existing centralized electricity grids resulting in a decarbonized and sustainable energy supply future.

The IoE is based on standard and interoperable communication transceivers, gateways, and protocols that will allow a real-time balance between the local and the global generation and storage capability with the energy demand. It provides an innovative concept for power distribution, energy storage, grid monitoring, and communication. This will also allow a high level of consumer awareness and involvement. As critical infrastructures, smart grids elevate the importance and the criticality of the involved IoT network, emphasizing privacy and ethics as well, in addition to performance and security.

This Special Issue will bring together the contributions from researchers of various scientific areas aiming to define the importance of smart sensors in smart grids for the integration of distributed energy generation sources and storage systems through a decentralized measurement network based on contemporary cyber-physical systems communication and security technologies towards the transition to the energy of internet era. Original research papers, reviews, successful cases studies, and applications, as well as opinion papers with high-quality and novelty concerning “Smart Sensor Networks for Smart Grids” are more than welcome.

Topics of interest include, but are not limited to:

Smart grids and smart sensors
Distributed ledger technologies and energy trading
Smart sensors and smart meters
Energy trading and the energy internet
Home energy management system and smart sensors
Smart grid and cyber-physical systems
AMI
Smart grids and IoT technologies
Smart grids and distributed renewable energy sources
Vehicle to grid energy storage and smart sensors
Wireless sensor networks in smart grids
Smart sub-metering

Prof. Dr. Panagiotis Papageorgas
Prof. Dr. Georgios Vokas
Prof. Dr. Constantinos S. Psomopoulos
Prof. Dr. Radu A. Munteanu
Dr. Kyriakos Agavanakis
Guest Editors

Manuscript Submission Information

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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 Grids and Smart Sensors
Distributed Ledger Technologies and Energy Trading
Smart Sensors and Smart Meters
Energy Trading and the Energy Internet
Home Energy Management System and Smart Sensors
Smart Grid and Cyber-Physical Systems
AMI
Smart Grids and IoT Technologies
Smart Grids and Distributed Renewable Energy Sources
Vehicle to Grid Energy Storage and Smart Sensors
Wireless Sensor Networks in Smart Grids
Smart Sub-Metering

Published Papers (8 papers)

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Research

28 pages, 4956 KiB

Open AccessArticle

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

by Abdul Azeem, Idris Ismail, Syed Muslim Jameel, Fakhizan Romlie, Kamaluddeen Usman Danyaro and Saurabh Shukla

Sensors 2022, 22(12), 4363; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124363 - 09 Jun 2022

Cited by 10 | Viewed by 2468

Abstract

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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16 pages, 1256 KiB

Open AccessArticle

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

by Renbo Wu and Shuqin Liu

Sensors 2022, 22(12), 4321; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124321 - 07 Jun 2022

Cited by 4 | Viewed by 1495

Abstract

With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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18 pages, 771 KiB

Open AccessArticle

False Data Injection Detection for Phasor Measurement Units

by Saleh Almasabi, Turki Alsuwian, Muhammad Awais, Muhammad Irfan, Mohammed Jalalah, Belqasem Aljafari and Farid A. Harraz

Sensors 2022, 22(9), 3146; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093146 - 20 Apr 2022

Cited by 6 | Viewed by 1528

Abstract

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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20 pages, 6560 KiB

Open AccessArticle

Design of a Supraharmonic Monitoring System Based on an FPGA Device

by Dimitris A. Barkas, George Ch. Ioannidis, Stavros D. Kaminaris and Constantinos S. Psomopoulos

Sensors 2022, 22(5), 2027; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052027 - 04 Mar 2022

Cited by 4 | Viewed by 2003

Abstract

During the last few decades, the poor quality of produced electric power is a key factor that has affected the operation of critical electrical infrastructure such as high-voltage equipment. This type of equipment exhibits multiple different failures, which originate from the poor electric power quality. This phenomenon is basically due to the utilization of high-frequency switching devices that operate over modern electrical generation systems, such as PV inverters. The conduction of significant values of electric currents at high frequencies in the range of 2 to 150 kHz can be destructive for electrical and electronic equipment and should be measured. However, the measuring devices that have the ability of analyzing a signal in the frequency domain present the ability of analyzing up to 2.5 kHz–3 kHz, which are frequencies too low in comparison to the high switching frequencies that inverters, for example, work. Electric currents at 16 kHz were successfully measured on an 8 kWp roof PV generator. This paper presents a fast-developed modern measuring system, using a field programmable gate array, aiming to detect electric currents at high frequencies, with a capability for working up to 150 kHz. The system was tested in the laboratory, and the results are satisfactory. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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18 pages, 5205 KiB

Open AccessArticle

Partial Discharge and Internet of Things: A Switchgear Cell Maintenance Application Using Microclimate Sensors

by Radu Fechet, Adrian I. Petrariu and Adrian Graur

Sensors 2021, 21(24), 8372; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248372 - 15 Dec 2021

Cited by 5 | Viewed by 3836

Abstract

This paper proposes a solution for the development of microclimate monitoring for Low Voltage/High Voltage switchgear using the PRTG Internet of Things (IoT) platform. This IoT-based real time monitoring system can enable predictive maintenance to reduce the risk of electrical station malfunctions due to unfavorable environmental conditions. The combination of humidity and dust can lead to unplanned electrical discharges along the isolators inside a low or medium voltage electric table. If no predictive measures are taken, the situation may deteriorate and lead to significant damage inside and outside the switchgear cell. Thus, the mentioned situation can lead to unprogrammed maintenance interventions that can conduct to the change of the entire affected switchgear cell. Using a low-cost and efficient system, the climate conditions inside and outside the switchgear are monitored and transmitted remotely to a monitoring center. From the results obtained using a 365-day time interval, we can conclude that the proposed system is integrated successfully in the switchgear maintaining process, having as result the reduction of maintenance costs. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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20 pages, 6772 KiB

Open AccessArticle

Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles

by Maria Fotopoulou, Dimitrios Rakopoulos and Orestis Blanas

Sensors 2021, 21(21), 7295; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217295 - 02 Nov 2021

Cited by 16 | Viewed by 3056

Abstract

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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28 pages, 4712 KiB

Open AccessArticle

A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

by Bilal Naji Alhasnawi, Basil H. Jasim, Zain-Aldeen S. A. Rahman and Pierluigi Siano

Sensors 2021, 21(14), 4756; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144756 - 12 Jul 2021

Cited by 38 | Viewed by 3261

Abstract

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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23 pages, 635 KiB

Open AccessArticle

An Appliance Scheduling System for Residential Energy Management

by Hanife Apaydin-Özkan

Sensors 2021, 21(9), 3287; https://0-doi-org.brum.beds.ac.uk/10.3390/s21093287 - 10 May 2021

Cited by 7 | Viewed by 2786

Abstract

In this work, an Appliance Scheduling-based Residential Energy Management System (AS-REMS) for reducing electricity cost and avoiding peak demand while keeping user comfort is presented. In AS-REMS, based on the effects of starting times of appliances on user comfort and the user attendance during their operations, appliances are divided into two classes in terms of controllability: MC-controllable (allowed to be scheduled by the Main Controller) and user-controllable (allowed to be scheduled only by a user). Use of all appliances are monitored in the considered home for a while for recording users’ appliance usage preferences and habits on each day of the week. Then, for each MC-controllable appliance, preferred starting times are determined and prioritized according to the recorded user preferences on similar days. When scheduling, assigned priorities of starting times of these appliances are considered for maintaining user comfort, while the tariff rate is considered for reducing electricity cost. Moreover, expected power consumptions of user-controllable appliances corresponding to the recorded user habits and power consumptions of MC-controllable appliances corresponding to the assigned starting times are considered for avoiding peak demand. The corresponding scheduling problem is solved by Brute-Force Closest Pair method. AS-REMS reduces the peak demand levels by 45% and the electricity costs by 39.6%, while provides the highest level of user comfort by 88%. Thus, users’ appliance usage preferences are sustained at a lower cost while their comfort is kept impressively. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Smart Grids)

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