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Advances in Sensor Technologies for Microgrid and Energy Storage
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Advances in Sensor Technologies for Microgrid and Energy Storage

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

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 4472

Special Issue Editors

Prof. Dr. Aman Maung Than Oo

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: microgrid & energy storage system; smart grid communication; power system stability; energy management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the extensive application of rooftop photovoltaic cells for supplying electrical energy for domestic as well as industrial consumption, the micro-grid is an emerging technology that is supporting significant transformation in economies and social networks worldwide. In the power system, various regional and state grid systems have begun to transition from being consumers of electricity to producing, sharing, and storing energy by deploying a smart micro-grid infrastructure.

The purpose of this Special Issue is connect researchers from various areas, all working on solving the challenges present in the application of the smart micro-grid in energy storage and monitoring, via smart sensors and sensing technology, in order to make the power supply network robust and failsafe. The aim is to discuss the following topics: (i) the development of smart sensors, sensing technology, WSN and the IoT for micro-grid and energy storage applications; (ii) recently developed machine learning and data mining techniques that can be employed to address the challenges of micro-grid integration; and (ii) practical research directions in the machine learning and artificial intelligence community in the area of micro-grid and energy storage.

Topics of interest include, but are not limited to, the following:

  • Smart sensors, sensing technology, sensors network in the smart micro-grid applications.
  • Hardware design of smart grids and energy storage system.
  • Challenges in the design, development and integration of smart energy storage systems.
  • Micro-grid identification and design.
  • Micro-grid energy management and control.
  • IoT-enabled smart sensor design, evaluation, and technologies for micro-grid and energy storage systems.
  • Machine learning and statistical methods for data mining in the domain of the micro-grid.
  • Power generation forecasts of renewable energy sources, storage and integration via micro-grids.
  • Integration of renewable energy sources into existing power grid and stability analyses.
  • Mining from the heterogeneous data sources of the micro-grid environment, including spatio-temporal, time-series, streaming, graph, and multimedia data.
  • Optimal placement and sizing of distributed generation sources in distributed micro-grid networks.
  • The analysis and design of micro-grid resilience.
  • Data mining for modeling and visualizing a micro-grid problem.
  • Decision-making and problem-solving networks in micro-grids.
  • Expert and knowledge-based systems for micro-grid development.
  • Security, privacy, and trust in micro-grid and energy storage environments.
  • Lightweight encryption and decryption algorithms that ensure micro-grid network security.
  • Cloud computing for IoT technology in micro-grid environments
  • Blockchain-based solutions for micro-grid and energy storage environments.
  • Design of next-generation systems using smart sensors for micro-grids and energy storage.
  • Architectures and algorithms for micro-grids and energy storage systems.
  • Automatic learning techniques in micro-grid security systems, smart grid networks and energy storage systems.

We particularly encourage the submission of articles attending to emerging topics of critical importance, such as smart sensing, wireless sensor networks, the Internet of Things, machine learning, deep learning, big data mining and analytics, smart grid systems, energy storage, renewable energy integration, heterogeneous data integration, and mining.

Prof. Dr. Aman Maung Than Oo
Prof. Dr. Subhas Mukhopadhyay
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 sensors
  • WSN
  • IoT
  • smart metering
  • smart grid
  • power system
  • renewable energy
  • energy storage
  • battery technologies
  • ICT for energy system
  • energy conversion
  • photovoltaic
  • solar panel

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Published Papers (4 papers)

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Research

27 pages, 11502 KiB  
Article
Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters
by Su-Chang Lim, Byung-Gyu Kim and Jong-Chan Kim
Sensors 2024, 24(19), 6390; https://doi.org/10.3390/s24196390 - 2 Oct 2024
Viewed by 635
Abstract
Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate [...] Read more.
Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies for Microgrid and Energy Storage)
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25 pages, 20796 KiB  
Article
Design and Feasibility Verification of Novel AC/DC Hybrid Microgrid Structures
by Jiaxuan Ren, Shaorong Wang and Xinchen Wang
Sensors 2024, 24(15), 4778; https://doi.org/10.3390/s24154778 - 23 Jul 2024
Viewed by 731
Abstract
To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics [...] Read more.
To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies for Microgrid and Energy Storage)
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18 pages, 6098 KiB  
Article
Photovoltaic Power Injection Control Based on a Virtual Synchronous Machine Strategy
by Miguel Albornoz, Jaime Rohten, José Espinoza, Jorge Varela, Daniel Sbarbaro and Yandi Gallego
Sensors 2024, 24(13), 4039; https://doi.org/10.3390/s24134039 - 21 Jun 2024
Viewed by 854
Abstract
The increasing participation of photovoltaic sources in power grids presents the challenge of enhancing power quality, which is affected by the intrinsic characteristics of these sources, such as variability and lack of inertia. This power quality degradation mainly generates variations in both voltage [...] Read more.
The increasing participation of photovoltaic sources in power grids presents the challenge of enhancing power quality, which is affected by the intrinsic characteristics of these sources, such as variability and lack of inertia. This power quality degradation mainly generates variations in both voltage magnitude and frequency, which are more pronounced in microgrids. In fact, the magnitude problem is particularly present in the distribution systems, where photovoltaic sources are spread along the grid. Due to the power converter’s lack of inertia, frequency problems can be seen throughout the network. Grid-forming control strategies in photovoltaic systems have been proposed to address these problems, although most proposed solutions involve either a direct voltage source or energy storage systems, thereby increasing costs. In this paper, a photovoltaic injection system is designed with a virtual synchronous machine control strategy to provide voltage and frequency support to the grid. The maximum power point tracking algorithm is adapted to provide the direct voltage reference and inject active power according to the droop frequency control. The control strategy is validated through simulations and key experimental setup tests. The results demonstrate that it is possible to inject photovoltaic power and provide voltage and frequency support. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies for Microgrid and Energy Storage)
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25 pages, 4574 KiB  
Article
Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network
by Yonggang Wang, Yilin Yao, Qiuying Zou, Kaixing Zhao and Yue Hao
Sensors 2024, 24(12), 3897; https://doi.org/10.3390/s24123897 - 16 Jun 2024
Cited by 2 | Viewed by 1219
Abstract
The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved [...] Read more.
The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network–bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network–bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies for Microgrid and Energy Storage)
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