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Journals
Processes
Special Issues
Approaches for the Design and Optimization of Wind Farms
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Approaches for the Design and Optimization of Wind Farms

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 2189

Special Issue Editors

Dr. Tian Li

E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing University, Chongqing, China
Interests: wind turbine aerodynamics; wind farm control; wind power prediction; wind farm optimization; off-shore wind turbine; fluid-structure interaction
Prof. Dr. Zhenqing Liu

E-Mail Website
Guest Editor
School of Civil Engineering & Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: wind farm simulation; wind turbine dynamics; off-shore floating wind turbine
Special Issues, Collections and Topics in MDPI journals
Dr. Weicheng Hu

E-Mail Website
Guest Editor Assistant
School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: wind engineering; CFD; complex terrain; wind farm micro-siting; deep learning

Special Issue Information

Dear Colleagues,

As the global demand for clean and renewable energy continues to rise, the optimization and efficient design of wind farms have become crucial in meeting energy targets and ensuring sustainable development. Wind energy, with its low environmental impact and abundant availability, is a cornerstone of renewable energy strategies. The integration of advanced design methodologies and optimization techniques can significantly enhance the performance, reliability, and economic viability of wind farms, thereby contributing to a more sustainable energy future.

This Special Issue on "Approaches for the Design and Optimization of Wind Farms" aims to collate cutting-edge research and innovative methodologies that address the challenges in the planning, design, and optimization of wind farms. The scope of this Special Issue spans a wide range of topics, including the application of computational fluid dynamics, aerodynamic modeling, structural analysis, site assessments, and resource forecasting. Additionally, it will cover advancements in control systems, energy storage integration, and the economic and environmental assessment of wind energy projects.

In order to maximize the impact of this Special Issue, authors are encouraged to share their models, simulations, and optimization tools with the broader community through open access repositories and/or as supplementary materials. This may include contributions such as computational models, optimization algorithms, simulation software, data sets, and other relevant digital objects that facilitate the design and analysis of wind farms.

Topics of interest include, but are not limited to, the following:

  • The aerodynamic and structural modeling of wind turbines;
  • Site assessment and resource forecasting techniques;
  • The optimization of wind farm layouts and turbine placement;
  • The integration of energy storage systems with wind farms;
  • Control systems for maximizing wind farms’ efficiency;
  • Economic and environmental impact assessments of wind energy projects;
  • Innovative simulation techniques and software for wind farm design;
  • Case studies and real-world applications of wind farm optimization.

We invite researchers, engineers, and practitioners to contribute their latest findings and insights to this Special Issue, in order to foster a collaborative effort to advance the field of wind farm design and optimization. Your participation will be instrumental in driving forward the sustainable development of wind energy.

Thank you and we look forward to your contributions to this Special Issue.

Dr. Tian Li
Prof. Dr. Zhenqing Liu
Guest Editors

Dr. Weicheng Hu
Guest Editor Assistant

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

  • wind turbines
  • wind turbine aerodynamics
  • wind resource forecasting
  • wind farm layout optimization
  • wind farm control
  • assessments of wind energy projects
  • wind farm simulation
  • wind farm design

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Published Papers (2 papers)

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14 pages, 2819 KiB  
Article
Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions–Sparrow Search Algorithm–Gated Recurrent Units
by Tongrui Yang, Xihao Guo and Guowei Qian
Processes 2024, 12(8), 1741; https://doi.org/10.3390/pr12081741 - 19 Aug 2024
Viewed by 735
Abstract
Improving the accuracy of short-term wind speed predictions is crucial for mitigating the impact on power systems when integrating wind power into an electricity grid. This study developed a hybrid short-term wind speed prediction method, termed VMD–SSA–GRU, by combining variational mode decomposition (VMD) [...] Read more.
Improving the accuracy of short-term wind speed predictions is crucial for mitigating the impact on power systems when integrating wind power into an electricity grid. This study developed a hybrid short-term wind speed prediction method, termed VMD–SSA–GRU, by combining variational mode decomposition (VMD) with gated recurrent units (GRUs) and optimizing it using a sparrow search algorithm (SSA). Initially, VMD was used to decompose the wind speed time series into subtime series. After reconstructing these subtime series, a GRU model was employed to establish separate prediction models for each series. Furthermore, an enhanced SSA was proposed to optimize the hyperparameters of the GRU model, which improved the prediction accuracy. Ultimately, the sub-series predictions were aggregated to produce the final wind speed prediction values. The predictive accuracy of this model was validated using the wind speed data measured at a meteorological station near a bridge site. The performance of the VMD–SSA–GRU model was compared with several other hybrid models, including those using wavelet transform, long short-term memory, and other neural networks. Comparably, the RMSE value of the VMD-SSA-GRU model was lower by 25.3%, 60.2%, and 61.7% in comparison to the VMD–SSA–LSTM, VMD–GRU, and VMD–LSTM models, respectively. The experimental results demonstrated that the proposed method achieved higher prediction accuracy than traditional methods. Full article
(This article belongs to the Special Issue Approaches for the Design and Optimization of Wind Farms)
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31 pages, 6558 KiB  
Article
Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization
by Weicheng Hu, Yan Zhang, Linya Liu, Pengfei Zhang, Jialiang Qin and Biao Nie
Processes 2024, 12(8), 1737; https://doi.org/10.3390/pr12081737 - 19 Aug 2024
Viewed by 892
Abstract
Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for [...] Read more.
Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects. Full article
(This article belongs to the Special Issue Approaches for the Design and Optimization of Wind Farms)
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