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Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants

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

Deadline for manuscript submissions: 5 May 2026 | Viewed by 1806

Special Issue Editor

Dr. Jesus C. Hernandez

E-Mail Website
Guest Editor
Electrical Engineering Department, University of Jaen, Campus Las Lagunillas, 23071 Jaen, Spain
Interests: power electronics; renewable systems; microgrids; electric vehicles; power quality; power systems simulation; metaheuristic optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies, titled “Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants”.

Electrical distribution networks have been rapidly transformed by the significant  integration of renewable energy sources, energy storage systems, and active power consumers. These changes require new methodologies to optimize, assess, and design these grids and renewable plants. Power electronics has emerged as a key technology in the conversion and control of electrical power in multiple renewable applications.

The main aim of this Special Issue is to seek high-quality contributions that address current issues related to more sustainable, safer, and more resilient distribution networks. Topics of interest include but are not limited to the following:

  • Solar, wind, and emerging generation technologies;
  • Control method of power electronic converters;
  • Optimization of operation of power systems;
  • Energy storage technologies;
  • Multi-phase distribution networks;
  • Direct current distribution networks;
  • Electric distributed systems;
  • Voltage stability and optimal line flow analysis;
  • Application of the IoT and/or AI for distribution networks.

Dr. Jesus C. Hernandez
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • distribution system modelling
  • optimization algorithms
  • renewable energies
  • distributed generation
  • systems and control for power electronic converters
  • hybrid AC/DC systems
  • distribution system planning and operation
  • IoT
  • AI

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Published Papers (3 papers)

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Research

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22 pages, 8029 KB  
Article
Early-Stage Fault Diagnosis for Batteries Based on Expansion Force Prediction
by Liye Wang, Yong Li, Yuxin Tian, Jinlong Wu, Chunxiao Ma, Lifang Wang and Chenglin Liao
Energies 2025, 18(24), 6619; https://doi.org/10.3390/en18246619 - 18 Dec 2025
Abstract
With the continuous expansion of the electric vehicle market, lithium-ion batteries have also been rapidly developed, but this has brought about concerns over the safety of lithium-ion batteries. Research on the correlation mechanism between the expansion and safety of lithium-ion batteries is a [...] Read more.
With the continuous expansion of the electric vehicle market, lithium-ion batteries have also been rapidly developed, but this has brought about concerns over the safety of lithium-ion batteries. Research on the correlation mechanism between the expansion and safety of lithium-ion batteries is a key step in the construction of a battery life cycle safety evaluation system. In this paper, the physicochemical mechanism of early safety faults in batteries was analyzed from three dimensions of electricity, heat, and force. The interactions of electrochemical side reactions, thermal runaway chain reactions, and mechanical fault mechanisms were analyzed, and the core induction of early safety risk was explored. A battery coupling model based on electrical, thermal, and mechanical dimensions was built, and the accuracy of the coupling model was verified by a variety of test conditions. Based on the coupling model, the stress distribution of the battery under different safety boundary conditions was simulated, and then the average expansion force of the battery surface was calculated through the stress distribution results. Through this process, a multi-parameter database based on the test and simulation data was obtained. According to the data of battery parameters at different times, an early safety classification method based on the battery expansion force was proposed, and a classification model between battery dimension data and safety level was proposed based on the nonlinear dynamic sparse regression method, and the classification accuracy was validated. From the perspective of fault warning, by establishing a multi-physical coupling model of electrical, thermal, and mechanical fields, the space-time evolution law of battery expansion under different working conditions can be dynamically monitored, and the fault criterion based on the expansion force can be established accordingly to provide quantitative indicators for safety risk classification warnings, and improve the battery’s reliability and durability. Full article
(This article belongs to the Special Issue Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants)
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20 pages, 3390 KB  
Article
Pattern-Aware BiLSTM Framework for Imputation of Missing Data in Solar Photovoltaic Generation
by Minseok Jang and Sung-Kwan Joo
Energies 2025, 18(17), 4734; https://doi.org/10.3390/en18174734 - 5 Sep 2025
Cited by 1 | Viewed by 1104
Abstract
Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems [...] Read more.
Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R2 value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R2 = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article
(This article belongs to the Special Issue Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants)
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Review

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17 pages, 267 KB  
Review
Graphene Nanoplatelets for Advanced Energy Storage Applications
by Aleksandra Tatara and Ewa Klugmann-Radziemska
Energies 2025, 18(23), 6326; https://doi.org/10.3390/en18236326 - 1 Dec 2025
Viewed by 248
Abstract
Graphene nanoplatelets (GNPs) represent a promising class of carbon nanomaterials bridging the gap between graphite and monolayer graphene. Their unique combination of high electrical conductivity, large specific surface area, mechanical strength, and chemical stability makes them attractive for advanced energy storage applications. This [...] Read more.
Graphene nanoplatelets (GNPs) represent a promising class of carbon nanomaterials bridging the gap between graphite and monolayer graphene. Their unique combination of high electrical conductivity, large specific surface area, mechanical strength, and chemical stability makes them attractive for advanced energy storage applications. This review summarizes recent developments in the synthesis, functionalization, characterization, and application of GNPs in supercapacitors, batteries, and hybrid systems. The influence of key structural parameters—such as flake thickness, lateral size, surface chemistry, and defect density—on electrochemical performance is discussed, highlighting structure–property correlations. Particular emphasis is placed on scalable production methods, including mechanical, liquid-phase, and electrochemical exfoliation, as well as edge functionalization and heteroatom doping strategies. Comparative analyses show that GNP-based electrodes can significantly improve specific capacitance, conductivity, and cycling stability, especially when used in composites with polymers or metal oxides. The review also addresses current challenges related to aggregation, dispersion, standardization, and environmental impact. Finally, prospects for the development of sustainable, low-emission GNP production and its integration into next-generation energy storage systems are outlined. Full article
(This article belongs to the Special Issue Renewable Energy Development in Distribution Networks: Optimization, Assessment and Design of Renewable Plants)
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