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Applications of Artificial Intelligence (AI) in Power Systems Operation Planning

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 525

Special Issue Editor


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Guest Editor
School of Engineering, Edith Cowan University, 270 Joondalup Dr., Joondalup, WA 6027, Australia
Interests: application of AI in power systems operation and control; power systems stability analysis; generator grid interconnections

Special Issue Information

Dear Colleagues,

Existing power grids are transforming at a tremendous pace due to rapid changes in both generation and load technology. In certain power grids, these changes have extended to include power transmission with the presence of advanced High-Voltage DC (HVDC) and Flexible AC Transmission Systems (FACTS) technology. These changes are experienced through increases in Inverter Based Resources (IBR) penetration, uptake in Electric Vehicles (EVs), expanding installations of Distributed Energy Resources (DER), and increased variable demand from large flexible loads such us data centers and crypto miners. Power system operators have already started investigating the applications of advanced tools in the study and planning of grid operations. While there is a need to provide accurate, fast, and low-cost solutions to these challenging grid conditions, applications of AI in the prediction and optimization of secure power operation conditions has been demonstrated to be a feasible option. However, due to the complication of the power system supply chain, there is a necessity for researching the applications of AI at all stages of power system operation planning. These research areas may include generation interconnection, system stability and configuration, energy market optimization, and distribution operation planning.

Dr. Thair Shaker Mahmoud
Guest Editor

Manuscript Submission Information

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Keywords

  • generator grid interconnection
  • flexible large load grid interconnection
  • system stability prediction
  • weak grid zone generator controller tunning
  • grid forming and grid following inverter control tunning
  • Intelligent Security Constrained Economic Dispatch (SCED)
  • Distributed Energy Resources (DERs) installation trend forecasting
  • energy market optimization
  • distribution energy market operation
  • distribution network operation planning

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Published Papers (1 paper)

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Research

17 pages, 984 KB  
Article
A Multi-Agent Closed-Loop Decision-Making Framework for Joint Forecasting and Bidding in Electricity Spot Markets
by Shicheng Zhang, Wangli Deng, Yuqin Zhang, Zhijun Jing, Ning Guo, Jianyu Yu, Bo Wang and Mei Liao
Energies 2025, 18(24), 6486; https://doi.org/10.3390/en18246486 - 11 Dec 2025
Abstract
With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated [...] Read more.
With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets. Full article
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