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Applications of Artificial Intelligence (AI) in Power Systems Operation Planning
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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: closed (20 May 2026) | Viewed by 3863

Special Issue Editor

Dr. Thair Shaker Mahmoud

E-Mail Website
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

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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 (3 papers)

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Research

26 pages, 4776 KB  
Article
Grid-Forming Inverters in Photovoltaic Power Systems: A Comprehensive Review of Modeling, Control, and Stability Perspectives
by Youness Hakam and Mohamed Tabaa
Energies 2026, 19(5), 1244; https://doi.org/10.3390/en19051244 - 2 Mar 2026
Cited by 1 | Viewed by 1309
Abstract
Grid-forming inverters (GFIs) are emerging as a key enabling technology for maintaining stability in renewable-dominated power systems, where conventional synchronous generation is progressively displaced by inverter-based resources. This paper presents a comprehensive technical review of GFI control strategies applied to photovoltaic (PV) systems, [...] Read more.
Grid-forming inverters (GFIs) are emerging as a key enabling technology for maintaining stability in renewable-dominated power systems, where conventional synchronous generation is progressively displaced by inverter-based resources. This paper presents a comprehensive technical review of GFI control strategies applied to photovoltaic (PV) systems, with focused attention on small-signal stability, transient dynamic performance, and overcurrent-limiting capabilities. In contrast to grid-following inverters (GFLIs), which rely on phase-locked-loop synchronization, GFIs operate as voltage sources capable of forming and regulating grid voltage and frequency. The reviewed control approaches, including droop control, virtual synchronous generator (VSG), synchronverter, matching control, virtual oscillator control (VOC), model predictive control (MPC), and intelligent techniques such as fuzzy logic control (FLC), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs), are systematically compared based on dynamic response characteristics, robustness under weak-grid conditions, control complexity, and practical implementation challenges. The paper synthesizes recent findings on stability margins, inertia emulation, transient current response, and protection requirements, highlighting remaining research gaps related to large-disturbance ride-through capability, coordination of multiple GFIs, and protection integration. These insights aim to support future deployments of reliable grid-forming photovoltaic systems in resilient inverter-dominated power networks. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Power Systems Operation Planning)
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17 pages, 7461 KB  
Article
Design and Real-Time Control of a Two-Switch Forward Converter-Based Photovoltaic Emulator for Accurate PV System Testing
by Mohamed Lamane, Youness Hakam and Mohamed Tabaa
Energies 2026, 19(1), 190; https://doi.org/10.3390/en19010190 - 30 Dec 2025
Viewed by 994
Abstract
This article describes the design, control, and implementation of a photovoltaic (PV) emulator using two-switch forward-converter topology. The system is designed to emulate the nonlinear electrical behavior of an actual PV panel under different environmental conditions including radiation level and temperature. The emulator [...] Read more.
This article describes the design, control, and implementation of a photovoltaic (PV) emulator using two-switch forward-converter topology. The system is designed to emulate the nonlinear electrical behavior of an actual PV panel under different environmental conditions including radiation level and temperature. The emulator provides galvanic isolation and also accurate current modulation to provide a safe yet reliable means of testing PV-related devices and algorithms within a laboratory setting. A dual-loop PI control is proposed to adjust the output current according to voltage feedback (VF), thus making accurate I–V and P–V curves achievable. Besides software simulation, a tailored printed circuit board (PCB) was fabricated. The simulation result demonstrated that the system can achieve a fast response and stable operation, with a maximum error percentage of about 2.1%, indicating high emulation fidelity, thereby providing an attractive platform for various evaluation purposes such as MPPT algorithms, inverters, and EMS. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Power Systems Operation Planning)
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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
Cited by 1 | Viewed by 673
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
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Power Systems Operation Planning)
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