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Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and...
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Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 988

Special Issue Editor

Dr. Ali S. Alghamdi

E-Mail Website
Guest Editor
Department of Electrical Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia
Interests: statistical DSP; adaptive filters; modern control in power systems; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth in the use of renewable energy technologies has transformed the global energy landscape, driving the need for innovative solutions to integrate these resources into existing power systems. This Special Issue focuses on the latest advancements in renewable energy conversion, grid optimization, and sustainable energy management. Key areas of interest include renewable energy forecasting, flexibility resources, smart grid technologies, and the integration of distributed energy resources. These advancements are critical for enhancing grid stability, improving energy efficiency, and achieving global sustainability goals.

This Special Issue, ‘Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management’, invites high-quality research articles, reviews, and case studies that address the following topics:

  • Renewable energy conversion technologies: Innovations in solar, wind, hydro, and bioenergy systems;
  • Grid integration and optimization: Challenges and solutions for integrating renewables into power grids and microgrids;
  • Flexibility resources: Energy storage systems, demand response, and virtual power plants;
  • Renewable energy forecasting: Advanced models for solar, wind, and other renewable energy sources;
  • Smart grid technologies: AI-driven grid management, real-time monitoring, and resilience enhancement;
  • Sustainable energy management: Policy frameworks, economic impacts, and lifecycle analysis of renewable energy systems;
  • Distributed generation: Reliability and electric vehicle grid-integrated systems.

We welcome contributions that highlight novel methodologies, practical applications, and interdisciplinary approaches toward advancing renewable energy systems and their integration into modern energy infrastructures.

Dr. Ali S. Alghamdi
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 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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • renewable energy integration
  • grid optimization
  • sustainable energy management
  • renewable energy forecasting
  • flexibility resources
  • smart grid technologies
  • energy storage systems
  • distributed energy resources
  • microgrids
  • AI in energy systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 2199 KiB  
Article
Optimal Integration of Distributed Generators and Soft Open Points in Radial Distribution Networks: A Hybrid WCA-PSO Approach
by Mohana Alanazi
Processes 2025, 13(6), 1775; https://doi.org/10.3390/pr13061775 - 4 Jun 2025
Viewed by 226
Abstract
The paper introduces a new hybrid optimization algorithm, HWCAPSO, for optimal distributed generator (DG) placement and soft-open point (SOP) size determination along with network reconfiguration. The hierarchical algorithm combining the Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) is introduced to solve [...] Read more.
The paper introduces a new hybrid optimization algorithm, HWCAPSO, for optimal distributed generator (DG) placement and soft-open point (SOP) size determination along with network reconfiguration. The hierarchical algorithm combining the Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) is introduced to solve this nonconvex problem. WCA excels in global exploration due to its water-cycle-inspired diversification, while PSO’s velocity-based update mechanism ensures rapid local convergence. Their hybrid synergy mitigates premature convergence in challenging problems. The proposed HWCAPSO algorithm uniquely integrates the global exploration capability of WCA with the local exploitation strength of PSO in a hierarchical framework, addressing the mixed-integer nonlinear programming (MINLP) challenges of simultaneous DG/SOP allocation and reconfiguration gap in existing hybrid methods. It aims to optimize total active power losses while fulfilling operational constraints such as voltage limits, thermal capacities, and radiality. The efficiency of the HWCAPSO is confirmed by exhaustive case studies from the 33-bus test system and the 69-bus test system, where its performance is compared with that of individual WCA and PSO. Findings show that HWCAPSO yields better loss reduction (up to 92.4% for the 33-bus network as and 92.7% for the 69-bus network), enhanced voltage profiles, as well as satisfactory convergence characteristics. Results are statistically validated over 30 independent runs, with 95% confidence intervals confirming robustness. The versatility of the algorithm to deal with intricate, multi-objective optimization applications make it an efficient option for real distribution network planning and operation. Full article
(This article belongs to the Special Issue Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management)
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22 pages, 3233 KiB  
Article
Improved Firefly Algorithm-Optimized ResNet18 for Partial Discharge Pattern Recognition Within Small-Sample Scenarios
by Yuhai Yao, Jun Gu, Tianle Li, Ying Zhang, Zihao Jia, Qiao Zhao and Jingrui Zhang
Processes 2025, 13(6), 1764; https://doi.org/10.3390/pr13061764 - 3 Jun 2025
Viewed by 215
Abstract
The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a [...] Read more.
The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a Firefly Algorithm with a Black Hole Mechanism-ResNet18 (FBH-ResNet18) framework that synergistically integrates the Firefly Algorithm with the Black Hole Mechanism (FBH algorithm) optimization with residual neural networks for PD signal classification using phase-resolved partial discharge (PRPD) mappings. A dedicated experimental platform first acquires PD signals through UHF sensors, which are subsequently converted into two-dimensional PRPD representations. The FBH algorithm systematically optimizes four key hyperparameters within the ResNet18 architecture during network training. The Black Hole Mechanism and improved population dynamics enhance optimization efficiency, resulting in more accurate hyperparameter tuning and improved model performance. Comparative evaluations demonstrate the enhanced performance of this parameter-optimized model against alternative configurations. Experimental results indicate that the improved ResNet18 achieves fast convergence and strong generalization on small-sample datasets, significantly enhancing recognition accuracy. During the first 80 generations of training, the classification accuracy reaches 89.11%, and in the final iteration, the model’s recognition accuracy increases to 92.55%, outperforming other models with accuracies generally below 90%. Additionally, the model shows excellent performance on the test set, with a loss function value of 0.250785, significantly lower than that of other models, indicating superior performance on small sample datasets. This research provides an effective solution for power cable fault diagnosis, offering high practical value. Full article
(This article belongs to the Special Issue Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management)
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36 pages, 3070 KiB  
Article
Optimized Coordination of Distributed Energy Resources in Modern Distribution Networks Using a Hybrid Metaheuristic Approach
by Mohammed Alqahtani and Ali S. Alghamdi
Processes 2025, 13(5), 1350; https://doi.org/10.3390/pr13051350 - 28 Apr 2025
Viewed by 307
Abstract
This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid [...] Read more.
This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes the global exploration capabilities of the Cheetah Optimizer (CO) with the local exploitation strengths of Grey Wolf Optimization (GWO). The optimization model addresses time-varying loads, renewable generation profiles, and dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, and SOP capacity. Simulations on a 33-bus distribution system demonstrate the effectiveness of the framework across eight case studies, with the full DER integration case (DSR + PV + ESS + SOP) achieving a 67.2% reduction in energy losses compared to the base configuration. By combining the global exploration of CO with the local exploitation of GWO, the hybrid CGWO algorithm outperforms traditional techniques (such as PSO and GWO) and avoids premature convergence while preserving computational efficiency—two major drawbacks of standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over standalone algorithms, yielding the lowest energy losses (997.41 kWh), balanced ESS utilization, and stable voltage profiles. The results underscore the transformative potential of coordinated DER optimization in enhancing grid efficiency and reliability. Full article
(This article belongs to the Special Issue Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management)
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