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Emerging AI Technologies in Renewable Power System Assessment, Control and Dispatching

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 10 July 2026 | Viewed by 2898

Special Issue Editor

1. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
2. School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China
Interests: power system planning; power system stability; integrated energy system; energy storage technology; low-carbon energy technolog
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Special Issue Information

Dear Colleagues,

Renewable power systems play an essential role in providing humanity a clean and low-carbon future for energy across the world. However, renewable energy remains uncertain and variable, leading to challenges in real-time power balancing and thus potentially jeopardizing the security, stability, and reliability of power grids. Recently, artificial intelligence (AI) methods have become a critical topic in the field, deployed in operational characteristic analysis of power grids. AI technologies, including machine learning, data mining, time series data analytics, and sensor networks, provide unlimited possibilities and opportunities for sustainable energy generation prediction, stability analysis and control, and power system dispatching. Incorporating AI, machine learning, and deep learning technologies into power system operation poses challenges and research gaps. The uncertainty and performance issues in current and future renewable power systems need to be addressed.

This Special Issue aims to provide a platform for researchers and scientists to share high-quality research solutions and outcomes for renewable power systems using emerging AI technologies. The focus is on both data-driven and physics-based techniques that aim to achieve more interpretable and efficient power system assessment, control, and dispatching methods.

Topics of interest for publication include, but are not limited to, the following:

  • Extreme scene generation based on knowledge–data fusion models;
  • Sustainable energy generation modeling and prediction;
  • Power system security situation awareness;
  • Stability analysis of power systems with high renewable energy penetration;
  • Intelligent evaluation of multi-type power system stability;
  • Security and stability control of hybrid AC/DC power systems;
  • Active support technology for grid-forming equipment;
  • Optimal power and energy system operation with uncertainty;
  • Multi-period economic dispatch modeling for smart grids;
  • AI method application in short-term dispatching.

Dr. Boyu Qin
Guest Editor

Manuscript Submission Information

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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

  • artificial intelligence
  • renewable power systems
  • stability analysis
  • coordinated control

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Published Papers (5 papers)

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Research

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18 pages, 7434 KB  
Article
Thermal Data Assimilation into a Real-Time Digital Twin of Liquid-Cooled Power Electronics via an Edge-Resident Particle Swarm Framework
by Braden Priddy, Josiah Worch, Kerry Sado, Richard Hainey, Austin R. J. Downey, Jamil Khan and Kristen Booth
Energies 2026, 19(10), 2452; https://doi.org/10.3390/en19102452 - 20 May 2026
Viewed by 241
Abstract
The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital [...] Read more.
The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article
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21 pages, 3132 KB  
Article
A Data-Driven Control Parameter Optimization Framework for Enhancing Frequency Stability in High-Renewable-Penetration Power Systems
by Lin Cheng, Fengrui Yang, Zhou Xing, Jing Ren, Zhe Zhang and Gengfeng Li
Energies 2026, 19(7), 1724; https://doi.org/10.3390/en19071724 - 1 Apr 2026
Viewed by 475
Abstract
As the penetration rate of renewable energy continues to rise, the equivalent inertia of power systems has significantly decreased, leading to a marked degradation in frequency stability support capabilities. Under conditions of high renewable energy penetration, the question of how to effectively enhance [...] Read more.
As the penetration rate of renewable energy continues to rise, the equivalent inertia of power systems has significantly decreased, leading to a marked degradation in frequency stability support capabilities. Under conditions of high renewable energy penetration, the question of how to effectively enhance grid frequency support capacity has become a critical research topic in the field of power system operation and control. This paper first systematically analyzes the impact of key control parameters on the frequency dynamic response of power systems. It investigates the intrinsic relationship between these parameters and system frequency stability through both analytical frequency response modeling and time-domain simulation analysis. A frequency stability margin metric is constructed based on the grid frequency response process to quantify the system’s frequency stability performance. Building upon this foundation, an improved ResNet-based frequency stability margin prediction model is established to enable rapid estimation of the frequency stability margin. Furthermore, Bayesian optimization is introduced to optimize frequency control parameters, thereby enhancing system frequency stability. Case studies conducted on the simulation system CSEE-FS with insufficient frequency support capability demonstrate that the proposed method effectively increases the frequency stability margin and significantly improves the system’s frequency response performance. Full article
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18 pages, 3430 KB  
Article
Intelligent Enhanced Method for Modern Power System Transient Voltage Stability Assessment Based on Improved Conditional Generative Adversarial Network
by Fan Li, Zhe Zhang, Hanqing Liang, Guodong Guo, Yuan Si and Yawei Xue
Energies 2026, 19(7), 1684; https://doi.org/10.3390/en19071684 - 30 Mar 2026
Viewed by 405
Abstract
The increasing complexity and variability of operating conditions, along with the occurrence of low-probability cascading failures, imposes more stringent requirements on data-driven intelligent methods for power system stability analysis. This paper proposes an intelligent enhancement approach for transient voltage stability assessment in modern [...] Read more.
The increasing complexity and variability of operating conditions, along with the occurrence of low-probability cascading failures, imposes more stringent requirements on data-driven intelligent methods for power system stability analysis. This paper proposes an intelligent enhancement approach for transient voltage stability assessment in modern power systems, considering improved conditional generative adversarial network (CGAN)-based sample balancing. Firstly, an improved CGAN incorporating an enhanced feature-distance metric is developed to accurately capture the distribution characteristics of real samples, effectively alleviating training issues such as gradient vanishing and mode collapse during adversarial learning. Secondly, an intelligent sample enhancement method for transient voltage stability is established based on the improved CGAN, which effectively complements the initial dataset and ensures the predictive performance of intelligent models under extreme operating conditions. Finally, a transient voltage stability assessment framework integrating a convolutional neural network and a transformer is proposed to enable efficient extraction of low-dimensional features and achieve accurate evaluation of transient voltage stability states. Full article
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Review

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51 pages, 2241 KB  
Review
Mathematical Analysis Methods for Quantitative Scenario Generation of Renewable Power Output: A Comprehensive Review
by Tong Ma, Boyu Qin, Shidong Hong and Yiwei Su
Energies 2026, 19(7), 1701; https://doi.org/10.3390/en19071701 - 31 Mar 2026
Viewed by 605
Abstract
As the proportion of renewable power continues to increase, its inherent intermittency and volatility pose serious challenges to the security and stability of power systems. Scenario generation technology serves as a key tool supporting decision-making methods such as stochastic optimization and risk analysis. [...] Read more.
As the proportion of renewable power continues to increase, its inherent intermittency and volatility pose serious challenges to the security and stability of power systems. Scenario generation technology serves as a key tool supporting decision-making methods such as stochastic optimization and risk analysis. By generating representative power output scenarios, it can effectively characterize the uncertainty of renewable power output. This paper systematically reviews mainstream methods for the scenario generation of renewable power output, categorizing them into two major classes: sampling-based methods and model-based methods. Among them, sampling-based methods include Monte Carlo sampling, Latin hypercube sampling (LHS), Markov chains (MCs), and Copula functions. Model-based methods encompass artificial neural networks (ANNs), long short-term memory networks (LSTMs), autoregressive moving average models (ARMAs), generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and transformer-based models. This paper elaborates on the principles and characteristics of each type of method. Moreover, scenario quality is evaluated from three dimensions: output-based metrics for numerical accuracy, distribution-based metrics for statistical consistency, and event-based metrics for key operational event representation. The current research challenges and future research directions are also summarized to provide a reference for modeling the uncertainty of renewable output. Full article
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27 pages, 2312 KB  
Review
Artificial Intelligence and Interpretability for Stability Assessment of Modern Power Systems: Applications and Prospects
by Fan Li, Zhe Zhang, Jishuo Qin, Taikun Tao, Dan Wang and Zhidong Wang
Energies 2026, 19(6), 1494; https://doi.org/10.3390/en19061494 - 17 Mar 2026
Viewed by 812
Abstract
The large-scale integration of renewable energy sources and power-electronic-interfaced devices has significantly weakened transient support capability and disturbance tolerance, posing new challenges to the secure and stable operation of modern power systems. Conventional stability analysis methods suffer from high computational burden, long execution [...] Read more.
The large-scale integration of renewable energy sources and power-electronic-interfaced devices has significantly weakened transient support capability and disturbance tolerance, posing new challenges to the secure and stable operation of modern power systems. Conventional stability analysis methods suffer from high computational burden, long execution time, and limited adaptability to diverse operating scenarios. The rapid development of artificial intelligence (AI) provides effective technical support for fast and accurate assessment of power-system security and stability. This paper presents a comprehensive review of AI-based methods and the interpretability for transient stability assessment (TSA) in modern power systems. First, an intelligent TSA framework is introduced, consisting of three key stages: sample construction and enhancement, intelligent algorithms and learning mechanisms, and model training and interpretability. Subsequently, existing methods for data augmentation, intelligent algorithms, learning mechanisms, and interpretability analysis are systematically reviewed, and the corresponding application scene, technical superiority and limitations are discussed. Finally, from a knowledge–data fusion perspective, four representative integration paradigms combining mechanism-based models and data-driven approaches are summarized, and the application prospects in power-system stability analysis are discussed. Full article
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