Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Advances in Optimal Control and Smart Operation of Renewable Energy...
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Optimal Control and Smart Operation 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: 28 February 2025 | Viewed by 1891

Special Issue Editors

Prof. Dr. Zhihuai Xiao

E-Mail Website
Guest Editor
Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
Interests: power generation system modeling; simulation and optimization control; power generation equipment condition monitoring; fault diagnosis and health management; big data, deep learning and artificial intelligence application research
Dr. Dong Liu

E-Mail Website
Guest Editor
College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: hydropower; simulation and modeling; performance evaluation; diagnosis and control; coordinated operation
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Zheng

E-Mail Website
Guest Editor
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Interests: low-frequency oscillations in power systems; pumped storage technology; applied hydraulic transients; modeling and control of hydropower units
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Ren

E-Mail Website
Guest Editor
College of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: smart energy; energy storage and new energy generation; multi-energy complementary technology
Dr. Jingjing Zhang

E-Mail Website
Guest Editor
College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: stability and regulation of hydropower units; co-generation of hydropower and new energy systems

Special Issue Information

Dear Colleagues,

The United Nations Sustainable Development Goals (SDGs) and carbon emission reduction measures have led to the rapid development of renewable energy worldwide. However, it is difficult to ignore the impact of the large-scale grid-connected operation of renewable energy on grid security and stability. On one hand, as they are often affected by natural resources and extreme weather, wind, solar and other renewable energies are intermittent and stochastic, leading to grid scheduling difficulties. On the other hand, hydropower is a power renewable energy source with flexible regulation capability; thus, it will play an increasingly important role in the new power system.

To build future-oriented smart and strong power grids, this Special Issue will publish research on the safe and stable operation of renewable energy systems, focusing on areas such as reporting the latest advances in the modelling, stability, control, diagnostics, assessment, and prediction of hydropower, wind power, and photovoltaic power, as well as their hybrid systems. This Special Issue will provide a broad communication platform for scholars in the field, as well as provide energy policy makers and power plant operators with advice and recommendations for the efficient operation of renewable energy systems.

Prof. Dr. Zhihuai Xiao
Dr. Dong Liu
Dr. Yang Zheng
Prof. Dr. Yan Ren
Dr. Jingjing Zhang
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

  • hydropower
  • wind power
  • photovoltaic power generation
  • nonlinear modelling
  • stability analysis
  • fault diagnosis
  • trend prediction
  • state assessment
  • optimal control
  • system integration
  • multi-energy complementarity
  • multi-field coupling
  • system capacity allocation
  • joint dispatch optimization
  • artificial intelligence
  • optimization algorithm

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 7314 KiB  
Article
A Fusion Model for Predicting the Vibration Trends of Hydropower Units
by Dong Liu, Youchun Pi, Zhengyang Tang, Hongpeng Hua and Xiaopeng Wang
Energies 2024, 17(23), 5847; https://doi.org/10.3390/en17235847 - 22 Nov 2024
Viewed by 257
Abstract
Hydropower units are essential to the safe, stable, and efficient operation of modern power systems, particularly given the current expansion of renewable energy systems. To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance [...] Read more.
Hydropower units are essential to the safe, stable, and efficient operation of modern power systems, particularly given the current expansion of renewable energy systems. To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance the accuracy and reliability of vibration trend prediction models. This paper proposes a fusion model for the vibration signal trend prediction of hydropower units based on the waveform extension method empirical mode decomposition (W-EMD) and long short-term memory neural network (LSTMNN). The fusion model first employed a waveform matching extension method based on parameter ergodic optimization to extend the original signal. Secondly, EMD was used to decompose the extended signal sequence and reconstruct the decomposition components by the extreme point division method, and the reconstructed high- and low-frequency components were used as LSTMNN inputs for component prediction. Finally, the component prediction results were superimposed with equal weights to obtain the predicted value of the vibration signal trend of the hydropower unit. The experimental results showed that the W-EMD signal decomposition method can effectively suppress the endpoint effect problem in the traditional EMD algorithm, improving the quality of EMD decomposition. Furthermore, through a case study of the upper guide X direction swing signal on the 16F unit of a domestic hydropower station, it was found that the proposed fusion model successfully predicted anomalies in the unit’s swing signals; compared with SVR, KELM, LSTMNN, and EMD + LSTMNN, the prediction accuracy was improved by 78.94%, 66.67%, 55.56%, and 42.86%, respectively. Full article
(This article belongs to the Special Issue Advances in Optimal Control and Smart Operation of Renewable Energy Systems)
►▼ Show Figures

Figure 1

25 pages, 22413 KiB  
Article
Fault Diagnosis Method for Hydropower Station Measurement and Control System Based on ISSA-VMD and 1DCNN-BiLSTM
by Lin Wang, Fangqing Zhang, Jiefei Wang, Gang Ren, Dengxian Wang, Ling Gao and Xingyu Ming
Energies 2024, 17(22), 5686; https://doi.org/10.3390/en17225686 - 14 Nov 2024
Viewed by 302
Abstract
Sudden failures of measurement and control circuits in hydropower plants may lead to unplanned shutdowns of generating units. Therefore, the diagnosis of hydropower station measurement and control system poses a great challenge. Existing fault diagnosis methods suffer from long fault identification time, inaccurate [...] Read more.
Sudden failures of measurement and control circuits in hydropower plants may lead to unplanned shutdowns of generating units. Therefore, the diagnosis of hydropower station measurement and control system poses a great challenge. Existing fault diagnosis methods suffer from long fault identification time, inaccurate positioning, and low diagnostic efficiency. In order to improve the accuracy of fault diagnosis, this paper proposes a fault diagnosis method for hydropower station measurement and control system that combines variational modal decomposition (VMD), Pearson’s correlation coefficient, a one-dimensional convolutional neural network, and a bi-directional long and short-term memory network (1DCNN-BiLSTM). Firstly, the VMD parameters are optimised by the Improved Sparrow Search Algorithm (ISSA). Secondly, signal decomposition of the original fault signals is carried out by using ISSA-VMD, and meanwhile, the optimal intrinsic modal components (IMFs) are screened out by using Pearson’s correlation coefficient, and the optimal set of components is subjected to signal reconstruction in order to obtain the new signal sequences. Then, the 1DCNN-BiLSTM-based fault diagnosis model is proposed, which achieves accurate diagnosis of the faults of hydropower station measurement and control system. Finally, experimental verification reveals that, in comparison with other methods such as 1DCNN, BiLSTM, ELM, BP neural network, SVM, and DBN, the proposed approach in this paper achieves an exceptionally high average recognition accuracy of 99.8% in both simulation and example analysis. Additionally, it demonstrates faster convergence speed, indicating not only its superior diagnostic precision but also its high application value. Full article
(This article belongs to the Special Issue Advances in Optimal Control and Smart Operation of Renewable Energy Systems)
►▼ Show Figures

Figure 1

18 pages, 3177 KiB  
Article
Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty
by Jia Lu, Jiaqi Zhao, Zheng Zhang, Yaxin Liu, Yang Xu, Tao Wang and Yuqi Yang
Energies 2024, 17(20), 5075; https://doi.org/10.3390/en17205075 - 12 Oct 2024
Viewed by 534
Abstract
Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is [...] Read more.
Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas. Full article
(This article belongs to the Special Issue Advances in Optimal Control and Smart Operation of Renewable Energy Systems)
►▼ Show Figures

Figure 1

29 pages, 13018 KiB  
Article
Suppression and Analysis of Low-Frequency Oscillation in Hydropower Unit Regulation Systems with Complex Water Diversion Systems
by Zhao Liu, Zhenwu Yan, Hongwei Zhang, Huiping Xie, Yidong Zou, Yang Zheng, Zhihuai Xiao and Fei Chen
Energies 2024, 17(19), 4831; https://doi.org/10.3390/en17194831 - 26 Sep 2024
Viewed by 511
Abstract
Low-frequency oscillation (LFO) poses significant challenges to the dynamic performance of hydropower unit regulation systems (HURS) in hydropower units sharing a tailwater system. Previous methods have struggled to effectively suppress LFO, due to limitations in governor parameter optimization strategies. To address this issue, [...] Read more.
Low-frequency oscillation (LFO) poses significant challenges to the dynamic performance of hydropower unit regulation systems (HURS) in hydropower units sharing a tailwater system. Previous methods have struggled to effectively suppress LFO, due to limitations in governor parameter optimization strategies. To address this issue, this paper proposes a governor parameter optimization strategy based on the crayfish optimization algorithm (COA). Considering the actual water diversion layout (WDL) of a HURS, a comprehensive mathematical model of the WDL is constructed and, combined with models of the governor, turbine, and generator, an overall HURS model for the shared tailwater system is derived. By utilizing the efficient optimization performance of the COA, the optimal PID parameters for the HURS controller are quickly obtained, providing robust support for PID parameter tuning. Simulation results showed that the proposed strategy effectively suppressed LFOs and significantly enhanced the dynamic performance of the HURS under grid-connected conditions. Specifically, compared to before optimization, the optimized system reduced the oscillation amplitude by at least 30% and improved the stabilization time by at least 25%. Additionally, the impact of the power grid system parameters on oscillations was studied, providing guidance for the optimization and tuning of specific system parameters. Full article
(This article belongs to the Special Issue Advances in Optimal Control and Smart Operation of Renewable Energy Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop