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Smart and Secure Energy Systems
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Smart and Secure Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 28 May 2024 | Viewed by 6938

Special Issue Editors

Prof. Dr. Sathyajith Mathew

E-Mail Website
Guest Editor
Faculty of Engineering and Sciences, University of Agder, 4879 Agder, Norway
Interests: wind energy; application of AI in energy systems
Prof. Dr. Mohan Lal Kolhe

E-Mail Website
Guest Editor
Faculty of Engineering and Science, University of Agder, P.O. Box 422, 4604 Kristiansand, Norway
Interests: clean energy technologies; renewable energy systems; electrical energy engineering; energy efficiency; energy economics; techno-economic operation of energy systems; renewable energy technologies integration; smart grids; micro grids; electric vehicles; energy storage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Axel Sikora

E-Mail Website
Guest Editor
Institute for Reliable Embedded Systems and Communication Electronics (ivESK), Offenburg University, Badstraße 24, D77652 Offenburg, Germany
Interests: embedded systems; secure communication; smart energy systems

Special Issue Information

Dear Colleagues,

The global power sector is evolving rapidly in order to adapt to the contemporary changes in the generation, transmission and consumption of electrical energy. Several factors, such as the high penetration of fluctuating renewable resources into the power grids, the increasing number of new loads (e.g., electric vehicles), the two-way flow of power due to distributed generation and potential power buy back policies, the presence of decentralized microgrids, the need for safe and secure communication between various system components, the increasingly dynamic energy markets etc., make the efficient management of modern power grids complex and challenging. The application of smart solutions and secure technologies is essential to addressing these challenges. This Special Issue on “Smart and Secure Energy Systems” aims at the dissemination of some selected developments in these emerging areas of energy research. Topics of interest include, but are not limited to:

  • Renewable energy resources and systems;
  • Optimal siting and sizing and performance analysis of renewable energy projects;
  • Power and load forecasts and optimal dispatch strategies;
  • Smart grid systems and components;
  • Microgrids;
  • Grid stability and power quality;
  • Electric vehicles;
  • Energy storage systems;
  • Controls and automation;
  • Efficient and secure communication;
  • IoT and energy systems;
  • Energy efficiency and management;
  • System optimization;
  • Applications of machine learning and artificial intelligence in energy systems;
  • Economic, environmental, and social aspects of energy conversion and consumption.

Prof. Dr. Sathyajith Mathew
Prof. Dr. Mohan Lal Kolhe
Prof. Dr. Axel Sikora
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. 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.

Published Papers (3 papers)

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Research

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23 pages, 3195 KiB  
Article
A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection
by Ankit Kumar Srivastava, Ajay Shekhar Pandey, Mohamad Abou Houran, Varun Kumar, Dinesh Kumar, Saurabh Mani Tripathi, Sivasankar Gangatharan and Rajvikram Madurai Elavarasan
Energies 2023, 16(2), 867; https://doi.org/10.3390/en16020867 - 12 Jan 2023
Cited by 6 | Viewed by 1764
Abstract
A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded [...] Read more.
A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets. Full article
(This article belongs to the Special Issue Smart and Secure Energy Systems)
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15 pages, 5259 KiB  
Article
Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems
by Veena Raj, Sam-Quarcoo Dotse, Mathew Sathyajith, M. I. Petra and Hayati Yassin
Energies 2023, 16(2), 671; https://doi.org/10.3390/en16020671 - 06 Jan 2023
Cited by 8 | Viewed by 1538
Abstract
In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) [...] Read more.
In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications. Full article
(This article belongs to the Special Issue Smart and Secure Energy Systems)
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Review

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23 pages, 5309 KiB  
Review
Computational Fluid Dynamics for Protonic Ceramic Fuel Cell Stack Modeling: A Brief Review
by Anitha Dhanasekaran, Yathavan Subramanian, Lukman Ahmed Omeiza, Veena Raj, Hayati Pg Hj Md Yassin, Muhammed Ali SA and Abul K. Azad
Energies 2023, 16(1), 208; https://doi.org/10.3390/en16010208 - 25 Dec 2022
Cited by 6 | Viewed by 2553
Abstract
Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-ion conducting solid oxide fuel cells (O-SOFCs) because they are simplified, [...] Read more.
Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-ion conducting solid oxide fuel cells (O-SOFCs) because they are simplified, have a longer life, and have faster startup times. A clear understanding/analysis of their specific working parameters/processes is required to enhance the performance of PCFCs further. Many physical processes, such as heat transfer, species transport, fluid flow, and electrochemical reactions, are involved in the operation of the PCFCs. These parameters are linked with each other along with internal velocity, temperature, and electric field. In real life, a complex non-linear relationship between these process parameters and their respective output cannot be validated only using an experimental setup. Hence, the computational fluid dynamics (CFD) method is an easier and more effective mathematical-based approach, which can easily change various geometric/process parameters of PCFCs and analyze their influence on its efficiency. This short review details the recent studies related to the application of CFD modeling in the PCFC system done by researchers to improve the electrochemical characteristics of the PCFC system. One of the crucial observations from this review is that the application of CFD modeling in PCFC design optimization is still much less than the traditional O-SOFC. Full article
(This article belongs to the Special Issue Smart and Secure Energy Systems)
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