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AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems—2nd Edition

A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: 25 February 2027 | Viewed by 854

Editors


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Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power systems; renewable energy integration; AI; cyber–physical security; climate resilience; risk management
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Guest Editor
College of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Interests: power flow control; integrated energy system; stability analysis and control
Special Issues, Collections and Topics in MDPI journals
School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Interests: electricity market; power system resilience; power system operation and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of the accelerating integration of large-scale renewable energy sources, the operation of modern power systems must adapt to maintain stability, reliability, and economic viability. As more wind, solar, and other renewables join the grid, operators confront fluctuating power generation and limited traditional dispatchable resources. Real-time monitoring, forecasting, and advanced control have become essential for handling intermittency and ensuring power quality. Although the modernization of grid infrastructure enables flexible load management and encourages new regulatory and market frameworks, digitization introduces potential vulnerabilities, underscoring the importance of basic cyber–physical security measures.

This Special Issue addresses these evolving challenges by showcasing state-of-the-art research in AI-enhanced power system operation and management with renewable energy integration technology. Through this collection of cutting-edge studies, this Special Issue aims to foster a deeper understanding of advanced operational and managerial methodologies, thereby accelerating the global transition toward a cleaner, more efficient, and resilient energy future.

Dr. Jiaqi Ruan
Dr. Yujia Huang
Dr. Chao Yang
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-anonymized 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

  • advanced AI applications
  • AI-enhanced operation and management methods
  • large-scale renewable energy integration
  • multi-energy coupling and coordination
  • demand-side management and demand response
  • real-time monitoring and analytics
  • cyber–physical security and resilience
  • risk assessment and management
  • distributed generation and microgrid operation

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Published Papers (1 paper)

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Research

21 pages, 4937 KB  
Article
Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine
by Houyu He and Yifa Sheng
Energies 2026, 19(10), 2350; https://doi.org/10.3390/en19102350 - 13 May 2026
Viewed by 490
Abstract
Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). [...] Read more.
Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model’s robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. Full article
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