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Electronics
Special Issues
Forecasting and Optimization Methods for High Renewable Penetration Power Systems
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Forecasting and Optimization Methods for High Renewable Penetration Power Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 22 July 2026 | Viewed by 793

Special Issue Editors

Dr. Qiangang Jia

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: electricity market; machine learning; distribution network operation; demand response
Dr. Zhuoxin Lu

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: distribution network economic operation; mobile energy storage
Dr. Peng Zhuang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: cyber–physical energy systems; microgrids and distributed energy systems; reinforcement learning; demand response; electricity market

Special Issue Information

Dear Colleagues,

With the increasing integration of renewable energy sources such as wind and solar, along with distributed generation, energy storage, and flexible loads, traditional power systems are facing new challenges. The high penetration of renewable energy leads to increased uncertainties, fluctuations, and complex operational constraints, making advanced forecasting and optimization technologies essential.

This Special Issue, entitled “Forecasting and Optimization Methods for High Renewable Penetration Power Systems”, will focus on new theories, models, algorithms, and practical applications for the forecasting, optimization, planning, operation, and control of systems with high levels of renewable energy integration. Our goal is to explore how advanced forecasting and optimization methods can support efficient, reliable, and low-carbon operation in these evolving power systems.

We invite contributions that present innovative solutions and real-world case studies and discuss challenges such as scalability, data quality, and operational reliability. This Special Issue will provide a platform for both academic and industry researchers to share insights into improving the operation of high-penetration renewable energy systems.

Dr. Qiangang Jia
Dr. Zhuoxin Lu
Dr. Peng Zhuang
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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 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

  • forecasting
  • optimization
  • renewable energy systems
  • energy storage
  • demand response
  • mathematical programming
  • stochastic optimization
  • low-carbon operation
  • real-time control
  • grid stability
  • energy management
  • system resilience

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Published Papers (1 paper)

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Research

18 pages, 6531 KB  
Article
Multi-Step Short-Term Forecasting of Photovoltaic Power Utilizing Autoformer with Prophet
by Kang Yang, Congmei Jiang and Yangming Min
Electronics 2026, 15(7), 1370; https://doi.org/10.3390/electronics15071370 - 26 Mar 2026
Cited by 1 | Viewed by 445
Abstract
The prediction of photovoltaic (PV) power generation faces certain challenges, primarily due to the high uncertainty of solar irradiance. The accuracy of PV power prediction is critical for the stability and reliability of power grids. However, existing models often perform poorly in long-sequence, [...] Read more.
The prediction of photovoltaic (PV) power generation faces certain challenges, primarily due to the high uncertainty of solar irradiance. The accuracy of PV power prediction is critical for the stability and reliability of power grids. However, existing models often perform poorly in long-sequence, multi-step prediction tasks, and there is still room for improvement in feature extraction from historical data. Therefore, this study proposes a novel forecasting method based on Autoformer and Prophet, combining the advantages of Autoformer in long-term sequence prediction with the strengths of Prophet in feature extraction to enhance the accuracy of PV power generation forecasting. First, the Autoformer encoder extracts seasonal components from complex time series data, while the decoder continuously utilizes the past seasonal components provided by the encoder for optimization. Then, Prophet extracts trend-cycle and seasonal components from the time series data input into the decoder. Finally, Autoformer predicts photovoltaic power generation based on the extracted features. The feasibility and superiority of the hybrid model are verified by comparing it with other models. The results show that the proposed method performs well across various performance evaluation metrics in the short-term PV prediction tasks, significantly outperforming other approaches. Full article
(This article belongs to the Special Issue Forecasting and Optimization Methods for High Renewable Penetration Power Systems)
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