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Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 1992

Special Issue Editors

Dr. Yikui Liu

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, China
Interests: optimal operation of power systems; power market
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Li

E-Mail Website
Guest Editor
School of Electrical Engineering and Information, Southwest Petroleum University, No. 8 Xindu Avenue, Xindu District, Chengdu 610500, China
Interests: power system optimal operation; energy system status monitoring and intelligent perception
Dr. Jinjing Tan

E-Mail
Guest Editor
School of Electric Power Engineering, Nanjing Institute of Technology, No. 1 Hongjing Road, Jiangning District, Nanjing 211167, China
Interests: electricity market; demand response; aggregation of distributed energy resources

Special Issue Information

Dear Colleagues,

The global shift toward decarbonizing power systems has led to a significant increase in the penetration of renewable energy, particularly from wind and solar energy sources. While these renewable energy sources play a crucial role in reducing greenhouse gas emissions, their inherent variability and uncertainty pose new challenges in maintaining grid reliability and stability. Traditionally, grid operators have relied on dispatchable fossil fuel plants to provide system flexibility, but the transition to a cleaner energy mix requires new sources of flexibility, particularly from distributed energy resources, energy storage, demand response, and other grid-responsive technologies. Smart grid technologies and evolving electricity markets are creating opportunities for those new sources to participate in grid services, offering financial incentives for flexibility. However, challenges such as technical coordination, regulatory frameworks, and market design remain.

This Special Issue invites original research articles addressing technical, economic, and policy considerations to enhance system reliability and efficiency. Topics of interest for this Special Issue include, but are not limited to, the following areas:

1) Optimization of distributed energy resources for renewable energy accommodation;
2) Demand Response Mechanisms to Support Renewable Energy Variability;
3) Energy Storage in Facilitating Renewable Energy Integration;
4) Electric Vehicles as Flexibility Resources for Renewable Integration;
5) Economic and Market Incentives for Distributed Flexibility providers;
6) Flexibility Market Design for Renewable Energy Accommodating;
7) Transmission and Distribution Coordination Networks for Renewable Energy Flexibility.

Dr. Yikui Liu
Dr. Qian Li
Dr. Jinjing Tan
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

  • renewable energy integration
  • grid flexibility
  • market incentives
  • distributed energy resources
  • energy storage systems
  • grid stability

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Published Papers (3 papers)

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Research

23 pages, 3699 KiB  
Article
Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior
by Shuyi Zhao, Chenshuo Ma and Zhiao Cao
Energies 2025, 18(3), 690; https://doi.org/10.3390/en18030690 (registering DOI) - 2 Feb 2025
Viewed by 325
Abstract
With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user [...] Read more.
With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility. Full article
(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)
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21 pages, 2201 KiB  
Article
Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling
by Yubo Wang, Chao Huo, Fei Xu, Libin Zheng and Ling Hao
Energies 2025, 18(1), 197; https://doi.org/10.3390/en18010197 - 5 Jan 2025
Viewed by 583
Abstract
The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage [...] Read more.
The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets. Full article
(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)
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19 pages, 3882 KiB  
Article
Research on Thyristor Reverse Recovery Behavior in High-Voltage Direct Current Transmission Converter Valves and Its Application in Integrated Protection Systems
by Cao Wen, Liang Song, Yu Huang, Dong Peng, Peng Zhang, Jianquan Liao, Longjie Yang and Shilin Gao
Energies 2024, 17(24), 6472; https://doi.org/10.3390/en17246472 - 23 Dec 2024
Viewed by 430
Abstract
The performance of converter valves is essential for the reliability and efficiency of high-voltage direct current (HVDC) transmission systems. Converter valves consist of multiple thyristor levels, each requiring regular testing to ensure proper functionality. Protective triggering tests play a crucial role in evaluating [...] Read more.
The performance of converter valves is essential for the reliability and efficiency of high-voltage direct current (HVDC) transmission systems. Converter valves consist of multiple thyristor levels, each requiring regular testing to ensure proper functionality. Protective triggering tests play a crucial role in evaluating the safety and performance of these thyristors during maintenance. This study introduces a high-power experimental setup designed to investigate the effects of varying current levels and thermal stresses on the reverse recovery behavior of thyristors—a key performance indicator. Results indicate that the reverse recovery time increases rapidly with higher current levels before reaching a saturation point. Additionally, prolonged exposure to high temperatures significantly reduces both the storage time and the amount of charge recovered during the reverse recovery process. These findings enable the optimization of protective test settings, thereby enhancing the effectiveness of the Thyristor Control Unit (TCU) in protecting converter valves. Improved testing methodologies derived from this research contribute to more reliable maintenance practices and increased overall stability of HVDC transmission systems. Full article
(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)
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