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Advanced Control Systems for Power Electronics, Smart Grids, and Renewable Energy Integration

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1171

Special Issue Editors

Dr. Yuying He

E-Mail Website
Guest Editor
School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Interests: modeling and control of grid-connected converters; harmonic analysis of power electronic power systems; stability of renewable energy generation system; optimal control of grid-connected inverters
Dr. Tingyu Jiang

E-Mail Website
Guest Editor
School of Electrical and Power Engineering, Hohai University, Nanjing 210000, China
Interests: energy management of power system; load modeling and regulation; power systems analysis and modeling; demand response

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on “Advanced Control Systems for Power Electronics, Smart Grids, and Renewable Energy Integration”.

Power electronics plays a significant role in modern industrial automation and high-efficiency energy systems. Optimization and control techniques are important for the efficient use of renewable-based power systems. This Special Issue will study novel optimization and control techniques for power electronics, smart grids, and renewable energy integration. Topics of interest for publication include, but are not limited to, the following:

  • Analysis, design, and control of power system;
  • Advanced control of grid-following and grid-forming inverters;
  • Artificial intelligence and digital twin technology in power systems;
  • Energy storage system;
  • Energy management system;
  • Grid integration and stability enhancement;
  • Control method of power electronics;
  • Advanced sensing and intelligent battery management system;
  • Microgrid energy conversion and control;
  • Cost-effective AC-DC and DC-DC converters;

Dr. Yuying He
Dr. Tingyu Jiang
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

  • power electronics
  • smart grid
  • power conversion
  • integration

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Published Papers (3 papers)

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Research

17 pages, 4736 KiB  
Article
Station-Aggregator Response Resource Trading Mechanism Considering Energy–Power Coupling of Response Capability
by Haiqing Gan, Wenjun Ruan, Xiaodong Yuan, Xize Jiao, Mingshen Wang and Yi Pan
Energies 2025, 18(11), 2990; https://doi.org/10.3390/en18112990 - 5 Jun 2025
Viewed by 145
Abstract
China’s electric vehicle (EV) fleet has reached 30 million, and the effective utilization of their charging and discharging capabilities can provide substantial regulation support to restore supply–demand balance. A prerequisite to achieving this benefit is the proper estimation and utilization of the regulation [...] Read more.
China’s electric vehicle (EV) fleet has reached 30 million, and the effective utilization of their charging and discharging capabilities can provide substantial regulation support to restore supply–demand balance. A prerequisite to achieving this benefit is the proper estimation and utilization of the regulation potential inherent in EVs. This paper focuses on charging stations and introduces a station-aggregator response resource trading mechanism considering energy–power coupling of response capability. First, the response capacity of charging stations is estimated from both energy and power perspectives. Next, trading behavior models are developed separately for charging stations and aggregators. Finally, a joint resource trading mechanism based on a Stackelberg game is proposed to coordinate responses between these entities. Simulation results validate the effectiveness of the proposed estimation method and the economic advantages of the trading mechanism, thereby realizing a win–win outcome. Full article
(This article belongs to the Special Issue Advanced Control Systems for Power Electronics, Smart Grids, and Renewable Energy Integration)
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30 pages, 3063 KiB  
Article
Operation Strategy of Multi-Virtual Power Plants Participating in Joint Electricity–Carbon Market Based on Carbon Emission Theory
by Jiahao Zhou, Dongmei Huang, Xingchi Ma and Wei Hu
Energies 2025, 18(11), 2820; https://doi.org/10.3390/en18112820 - 28 May 2025
Viewed by 368
Abstract
The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they [...] Read more.
The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article
(This article belongs to the Special Issue Advanced Control Systems for Power Electronics, Smart Grids, and Renewable Energy Integration)
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20 pages, 4646 KiB  
Article
A Federated Learning Algorithm That Combines DCScaffold and Differential Privacy for Load Prediction
by Yong Xiao, Xin Jin, Tingzhe Pan, Zhenwei Yu and Li Ding
Energies 2025, 18(6), 1482; https://doi.org/10.3390/en18061482 - 17 Mar 2025
Viewed by 322
Abstract
Accurate residential load forecasting plays a crucial role in optimizing demand-side resource integration and fulfilling the needs of demand-side response initiatives. To tackle challenges, such as data heterogeneity, constrained communication resources, and data security in smart grid load prediction, this study introduces a [...] Read more.
Accurate residential load forecasting plays a crucial role in optimizing demand-side resource integration and fulfilling the needs of demand-side response initiatives. To tackle challenges, such as data heterogeneity, constrained communication resources, and data security in smart grid load prediction, this study introduces a novel differential privacy federated learning algorithm. Leveraging the federated learning framework, the approach incorporates weather and temporal factors as key variables influencing load patterns, thereby creating a privacy-preserving load forecasting solution. The model is built upon the Long Short-Term Memory (LSTM) network architecture. Experimental results demonstrate that the proposed algorithm enables federated training without the need for sharing raw load data, facilitating load scheduling and energy management operations in smart grids while safeguarding user privacy. Furthermore, it exhibits superior prediction accuracy and communication efficiency compared to existing federated learning methods. Full article
(This article belongs to the Special Issue Advanced Control Systems for Power Electronics, Smart Grids, and Renewable Energy Integration)
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