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Energies
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Energy Management of Renewable Energy Systems
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Energy Management of Renewable Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (5 September 2025) | Viewed by 3346

Special Issue Editors

Prof. Dr. Daniel Tudor Cotfas

E-Mail Website
Guest Editor
Electrical Engineering and Computer Science Faculty, Transilvania University of Brasov, Eroilor, nr. 29, 500036 Brasov, Romania
Interests: photovoltaic systems; hybrid systems; energy harvesting; modeling of the photovoltaic cells and panels
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Petru Adrian Cotfas

E-Mail Website
Guest Editor
Electrical Engineering and Computer Science Faculty, Transilvania University of Brasov, Eroilor, nr. 29, 500036 Brasov, Romania
Interests: photovoltaic systems; hybrid systems characterization; concentrated light systems; hybrid system reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Depending on the type of application and its scale (utility-scale solar power plants, medium-scale commercial systems, building-integrated solar energy systems, or small-scale applications down to energy harvested for wearable devices and sensor networks), the design and management of the solar power system should be considered when addressing the energy needs of the application whether on-grid or off-grid, based on the geographic location of the site and other factors, aiming at the most cost-effective and competitive configuration with a long system lifetime. Recent research focuses on the management of the interdisciplinary, intelligent, and innovative configurations of renewable energy systems, contributing to increased efficiency, reliability, and overall system yield.

Potential topics include, but are not limited to, the following:

  • Innovations photovoltaic system;
  • Innovations thermal solar system;
  • Thermal management systems for photovoltaic cells and panels in natural and concentrated light;
  • Energy management of the small energy harvesting systems;
  • Management of the energy storage systems;
  • Solar hybrid power system management using Modular Multilevel Converter;
  • Reliability and feasibility studies and consideration of critical issues encountered in solar hybrid power systems;
  • Management of the grid integration of solar power systems;
  • Energy management of heating, ventilation, and air conditioning (HVAC) systems.

Prof. Dr. Daniel Tudor Cotfas
Dr. Petru Adrian Cotfas
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.

Keywords

  • energy management
  • photovoltaic systems
  • thermal systems
  • energy harvesting
  • energy storage
  • grid integration
  • HVAC systems

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Published Papers (3 papers)

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Research

30 pages, 3310 KB  
Article
Probabilistic Analysis of Solar and Wind Energy Potentials at Geographically Diverse Locations for Sustainable Renewable Integration
by Satyam Patel, N. P. Patidar and Mohan Lal Kolhe
Energies 2025, 18(22), 6076; https://doi.org/10.3390/en18226076 - 20 Nov 2025
Viewed by 76
Abstract
The use of conventional fuel sources from the Earth to generate electrical power leads to several environmental issues such as carbon emissions and ozone depletion. Energy generation from renewable energy sources is one of the most affordable and cleanest techniques. However, the generation [...] Read more.
The use of conventional fuel sources from the Earth to generate electrical power leads to several environmental issues such as carbon emissions and ozone depletion. Energy generation from renewable energy sources is one of the most affordable and cleanest techniques. However, the generation of power from non-conventional sources like solar and wind requires the examination of established locations where these resources are plentiful and easily accessible. In this study, an investigation of solar and wind is performed at five different sites in various locations in India. For this examination, data on solar irradiance (W/m2) and wind speed (m/s) is taken from the “NASA POWER DAV v.2.5.22” Data Access Viewer created by NASA. The data for solar and wind was taken at hourly intervals. The period of the investigation was ten years, i.e., from January 2014 to December 2023. The solar and wind potential analysis was performed in a probabilistic way to determine the parameters that support the installation of solar–PV panels and wind energy generators at the examined sites for the generation of power from these spontaneously available sources, respectively. To examine the potential of solar and wind sites, the Beta and Weibull probability distribution function (PDF) was used. The parameter estimation of the Beta and Weibull PDF was performed via the Maximum Likelihood method. The chosen method is known for its accuracy and efficiency in handling large datasets. Some key performance prediction indicators were analyzed for the investigated solar and wind locations. The findings provide valuable insights that support renewable energy planning and the optimal design of hybrid power systems. Full article
(This article belongs to the Special Issue Energy Management of Renewable Energy Systems)
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65 pages, 10186 KB  
Article
Maximizing Return on Investment in Cryptocurrency Mining Through Energy Optimization
by Mohammad Nasrinasrabadi, Maryam A. Hejazi, Arefeh Jaberi, Hamed Hashemi-Dezaki and Hossein Shahinzadeh
Energies 2025, 18(22), 5910; https://doi.org/10.3390/en18225910 - 10 Nov 2025
Viewed by 892
Abstract
Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing [...] Read more.
Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing a hybrid energy system consisting of distributed renewable and non-renewable energy sources, focusing on cryptocurrency mining. Although previous studies have not yet addressed energy system optimization considering cryptocurrency mining farms, the increasing prominence of such farms highlights the growing need for research in this area. The primary renewable sources in the proposed hybrid system include photovoltaic (PV) panels and wind turbines. We employ diesel generators as backup systems to compensate for the intermittent nature of solar and wind energy production. Besides meeting the demands of urban loads, cryptocurrency mining devices will be considered a major energy consumer. In this article, the optimal configuration of the energy system will be determined based on technical and economic indicators. Additionally, economic evaluations will be conducted to assess the income generated from cryptocurrency mining farms, and appropriate approaches will be identified from both technical and financial perspectives, focusing on return on investment (ROI). Full article
(This article belongs to the Special Issue Energy Management of Renewable Energy Systems)
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16 pages, 2583 KB  
Article
PV Generation Prediction Using Multilayer Perceptron and Data Clustering for Energy Management Support
by Fachrizal Aksan, Vishnu Suresh and Przemysław Janik
Energies 2025, 18(6), 1378; https://doi.org/10.3390/en18061378 - 11 Mar 2025
Cited by 4 | Viewed by 1227
Abstract
Accurate PV power generation forecasting is critical to enable grid utilities to manage energy effectively. This study presents an approach that combines machine learning with a clustering methodology to improve the accuracy of predictions for energy management purposes. First, various machine learning models [...] Read more.
Accurate PV power generation forecasting is critical to enable grid utilities to manage energy effectively. This study presents an approach that combines machine learning with a clustering methodology to improve the accuracy of predictions for energy management purposes. First, various machine learning models were compared, and multilayer perceptron (MLP) outperformed others by effectively capturing the complex relationships between weather parameters and PV power output, obtaining the following results: MSE: 3.069, RMSE: 1.752, and MAE: 1.139. To improve the performance of MLP, weather characteristics that are highly correlated with PV power outputs, such as irradiation and sun elevation, were grouped using K-means clustering. The elbow method identified four optimal clusters, and individual MLP models were trained on each, reducing data complexity and improving model focus. This clustering-based approach significantly improved the accuracy of the predictions, resulting in average metrics across all clusters of the following: MSE: 0.761, RMSE: 0.756, and MAE: 0.64. Despite these improvements, further research on optimizing the MLP architecture and clustering methodology is required to address inconsistencies and achieve even better performance. Full article
(This article belongs to the Special Issue Energy Management of Renewable Energy Systems)
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