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Energies
Special Issues
Renewable Energy Generation Forecasting and Electric Power Distribution Planning
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Renewable Energy Generation Forecasting and Electric Power Distribution Planning

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

Deadline for manuscript submissions: closed (25 July 2025) | Viewed by 1295

Special Issue Editors

Dr. José-Luis Casteleiro-Roca

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Guest Editor
CTC, Department of Industrial Engineering, CITIC, University of A Coruña, EPEF, Calle Mendizábal, s/n, Campus de Esteiro, Ferrol, 15403 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modelling, optimization and control; fault and anomalies detection using traditional and intelligent techniques; new sensors; robust sensors and virtual sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Zayas-Gato

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Ferrol, 15071 A Coruña, Spain
Interests: modelling, optimization, and control based on intelligent systems; fault and anomaly detection based on traditional and intelligent techniques; artificial intelligence; power energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Esteban Pérez

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Ferrol, 15071 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control; intelligent systems for modelling, optimization, and control; anomaly detection using traditional and intelligent techniques; new sensors; robust sensors and virtual sensors; artificial intelligence; power energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Álvaro Michelena Grandío

E-Mail Website
Guest Editor
CTC, Department of Industrial Engineering, CITIC, University of A Coruña, EPEF, Calle Mendizábal, s/n, Campus de Esteiro, 15403 Ferrol, A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modelling, optimization and control; fault and anomalies detection using traditional and intelligent photo in attachment

Special Issue Information

Dear Colleagues,

In the rapidly evolving field of renewable energy, the accurate forecasting of energy generation and the strategic planning of electric power distribution stand as cornerstone challenges. The Special Issue titled "Renewable Energy Generation Forecasting and Electric Power Distribution Planning" in the esteemed journal Energies invites pioneering research that addresses these critical issues. We seek contributions from experts, researchers, and practitioners who are pushing the boundaries of knowledge in the prediction of renewable energy outputs and the optimization of distribution networks to accommodate the dynamic nature of renewable sources. This Special Issue aims to gather a diverse range of studies, from innovative forecasting models that enhance accuracy and reliability to cutting-edge planning methodologies that ensure sustainable and efficient energy distribution. Whether your work involves solar, wind, hydro, or any other form of renewable energy, we encourage you to share your insights, methodologies, and results. Join us in shaping the future of energy systems by contributing your research to this vital discourse. Your expertise can lead to breakthroughs that propel us towards a more sustainable and resilient energy future.

Dr. José-Luis Casteleiro-Roca
Dr. Francisco Zayas-Gato
Dr. Esteban Jove Pérez
Dr. Álvaro Michelena Grandío
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

  • electric power distribution
  • energy forecasting
  • redistributable generation
  • renewable energy

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Published Papers (2 papers)

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Research

18 pages, 2579 KB  
Article
LSTM-Based Prediction of Solar Irradiance and Wind Speed for Renewable Energy Systems
by Ahmed A. Alguhi and Abdullah M. Al-Shaalan
Energies 2025, 18(17), 4594; https://doi.org/10.3390/en18174594 - 29 Aug 2025
Viewed by 491
Abstract
Renewable energy systems like solar and wind power are the main source of sustainable energy production; however, their intermittent nature produces challenges for grid integration, so they require realistic forecast models. This study developed a Long Short-Term Memory (LSTM) neural network model to [...] Read more.
Renewable energy systems like solar and wind power are the main source of sustainable energy production; however, their intermittent nature produces challenges for grid integration, so they require realistic forecast models. This study developed a Long Short-Term Memory (LSTM) neural network model to predict solar irradiance and wind power over a 24 h horizon using a 240 h (10-day) dataset. The dataset, being hourly measurements of solar irradiance (W/m2) and wind speed (m/s), was divided and normalized into 193 sequences of 24 h each, with 80% for training and 20% for validation. Two LSTM models, each consisting of 100 hidden units, were trained using the Adam optimizer to predict the next 24 h for each of the variables using forget, input, and output gates to capture temporal dependencies. The results have shown that the model accurately forecasted solar irradiance with a clear day–night cycle, while forecasts of wind speed revealed higher variability, although the PV system was better than the wind system due to low wind speeds. The results reveal that the LSTM model can effectively predict renewable energy output by predicting the wind speed and Solar Irradiance, which are the main parameters that control the output power of wind turbines and PV power, respectively. Full article
(This article belongs to the Special Issue Renewable Energy Generation Forecasting and Electric Power Distribution Planning)
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17 pages, 3088 KB  
Article
Optimal Distribution Planning of Solar Plants and Storage in a Power Grid with High Penetration of Renewables
by Pere Colet, Benjamín A. Carreras, José Miguel Reynolds-Barredo and Damià Gomila
Energies 2025, 18(15), 3891; https://doi.org/10.3390/en18153891 - 22 Jul 2025
Viewed by 327
Abstract
Integrating variable renewable energy sources such as solar power into existing power grids presents major planning and reliability challenges. This study introduces an approach to optimize the placement of solar plants and allocation of storage in grids with high share of these variable [...] Read more.
Integrating variable renewable energy sources such as solar power into existing power grids presents major planning and reliability challenges. This study introduces an approach to optimize the placement of solar plants and allocation of storage in grids with high share of these variable energy sources by using a simulation framework that captures system-wide emergent behaviors. Unlike traditional engineering models focused on detailed component-level dynamics, a modified ORNL-PSERC-Alaska model based on self-organized criticality is used to reproduce the statistical features of blackouts, including cascading failures and long-range correlations. A distinctive feature of this approach is the explicit inclusion of key ingredients that shape these statistics, such as the transmission grid structure, generation and consumer buses, power flow balance, periodic dispatches, system failures, secular demand growth, demand fluctuations, and variability of renewable energy sources. When applied to the Balearic Islands grid, this method identifies generation and storage layouts that minimize storage requirements while maintaining reliability levels comparable to conventional power systems. The results offer a complementary systems-level perspective for planning resilient and efficient renewable energy integration. Full article
(This article belongs to the Special Issue Renewable Energy Generation Forecasting and Electric Power Distribution Planning)
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