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Optimal Planning and Operation in RES-Rich Power Systems Under Electricity and Carbon Emission Market Environment: 2nd Edition

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 532

Special Issue Editors


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Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: smart grids and electric vehicles; power economics and electricity markets; power system investment, planning and operation optimization; power system alarm processing, fault diagnosis and system restoration
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, University of Shanxi, Taiyuan, China
Interests: operation and control of modern power systems; technology of energy storage; electric power market
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ever-increasing penetration of renewable energy sources (RESs), electric vehicles, and energy storage devices into a modern power system, power system planning and operation are facing new problems and challenges. The establishment of electricity markets and carbon emission markets makes planning and operation issues more complicated and more challenging. It is believed that the present power system will gradually evolve to one with extensive RES generation integration, and the role thermal generation units are currently playing will change accordingly. Given this background, this Special Issue will be devoted to research topics regarding optimal planning and operation in RES-rich power systems under electricity and a carbon emission market environment.

The topics to be covered in this Special Issue include but are not limited to the following:

  1. Power system planning;
  2. Power system operation;
  3. Electricity market mechanism for power systems with high-penetration renewable energy generation;
  4. Local electricity market and peer-to-peer trading;
  5. Potential evaluation, aggregated control, coordinated operation, and market mechanism of flexible resources;
  6. Energy storage systems and electric vehicles in modern power systems;
  7. Artificial intelligence, big data, and blockchain applications.

Prof. Dr. Fushuan Wen
Assoc. Prof. Xiuli Wang
Guest Editors

Manuscript Submission Information

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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 sources
  • electricity market
  • carbon emission market
  • power system
  • energy storage system
  • artificial intelligence

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Published Papers (1 paper)

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Research

17 pages, 2076 KiB  
Article
Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest
by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng and Yunguang Gao
Energies 2025, 18(15), 4088; https://doi.org/10.3390/en18154088 - 1 Aug 2025
Viewed by 221
Abstract
The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest [...] Read more.
The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration. Full article
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