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Technologies
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AI for Smart Grid Optimization—Technological Advances and Future Perspectives
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AI for Smart Grid Optimization—Technological Advances and Future Perspectives

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 2797

Special Issue Editors

Dr. Xiaoshun Zhang

E-Mail Website
Guest Editor
Foshan Graduate School of Innovation, Northeastern University, Foshan 528311, China
Interests: AI optimization; power system operation; control strategies; new energy control and optimiza-tion; low-carbon energy management
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenning Pan

E-Mail Website
Guest Editor
School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Interests: optimization and control of power system; artificial intelligence (AI) for power system operation; reinforcement learning; transactive energy; electric vehicles

Special Issue Information

Dear Colleagues,

To mark the growing significance of AI in advancing smart grid systems, we are launching a Special Issue entitled “AI for Smart Grid Optimization—Technological Advances and Future Perspectives”. This Special Issue will include high-quality papers on topics within the scope of AI applications in smart grids. We invite you to contribute an original research or comprehensive review article on a hot topic for peer review and possible publication.

In this Special Issue, original research articles and reviews are welcome. The scope of this Special Issue includes, but is not limited to, the following topics:

  • AI-driven power forecasting (short-term/long-term load prediction, renewable energy output forecasting, etc.);
  • Intelligent power dispatch (real-time optimal dispatch, multi-energy system coordination, demand response optimization, etc.);
  • Smart grid control (adaptive control strategies, fault diagnosis and self-healing, stability control, etc.);
  • AI-aided grid planning (network expansion planning, distributed energy resource integration planning, infrastructure upgrading, etc.);
  • Low-carbon monitoring and management (carbon emission tracking, energy efficiency optimization, green energy scheduling, etc.);
  • Large model technologies in smart grids (foundation models for multi-source data fusion, scalable algorithms for grid-wide optimization, etc.).

Dr. Xiaoshun Zhang
Dr. Zhenning Pan
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly 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 1800 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

  • AI-driven smart grid optimization
  • power forecasting and dispatch
  • smart power control
  • low-carbon power planning and operation
  • large models for power systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

35 pages, 4348 KB  
Article
An Integrated Forecasting and Scheduling Energy Management Framework for Renewable-Supported Grids with Aggregated Electric Vehicles
by Rania A. Ibrahim, Ahmed M. Abdelrahim, Abdelaziz Elwakil and Nahla E. Zakzouk
Technologies 2026, 14(3), 185; https://doi.org/10.3390/technologies14030185 - 19 Mar 2026
Viewed by 258
Abstract
The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand [...] Read more.
The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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26 pages, 1623 KB  
Article
Graph-Augmented Fault Diagnosis in Power Systems with Imbalanced Text Data: A Knowledge Extraction and Agent-Based Reasoning Framework
by Yipu Zhang, Yan Guo, Qingbiao Lin, Zhantao Fan, Shengmin Qiu, Xiaogang Wu and Xiaotao Fang
Technologies 2026, 14(3), 181; https://doi.org/10.3390/technologies14030181 - 17 Mar 2026
Viewed by 262
Abstract
Fault diagnosis in modern power systems increasingly depends on unstructured operation and maintenance (O&M) logs, yet real-world logs are often small in scale and highly imbalanced across fault types, which degrades the generalizability of standard neural models. This paper proposes a graph-augmented diagnostic [...] Read more.
Fault diagnosis in modern power systems increasingly depends on unstructured operation and maintenance (O&M) logs, yet real-world logs are often small in scale and highly imbalanced across fault types, which degrades the generalizability of standard neural models. This paper proposes a graph-augmented diagnostic framework that integrates imbalance-aware knowledge extraction with interpretable reasoning. The framework consists of three stages: (1) domain adaptation of a BERT–BiLSTM–CRF NER model and a BERT–MLP RE model using an imbalance-aware training recipe that combines Low-Rank Adaptation (LoRA), a mixed focal–range loss, and undersampling; (2) construction of a power-system knowledge graph that organizes extracted entities and relations (e.g., fault devices, abnormal phenomena, causes, and handling measures); and (3) a graph-augmented assistant agent that reuses the NER model as a graph-aware retriever within a retrieval-augmented generation (RAG) architecture to support contextualized and interpretable diagnostic reasoning. Experiments on 3921 real-world fault-processing logs show consistent gains: NER reaches 92.0% accuracy and 71.3% Macro-F1 (vs. 80.3% and 63.2%), and RE achieves 88.0% accuracy and 70.1% F1 (vs. 82.1% and 60.4%), while reducing average training time per epoch by about 18%. These results demonstrate an efficient and practical path toward robust log-based fault diagnosis under scarce and imbalanced data. Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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24 pages, 2462 KB  
Article
Two-Layer Low-Carbon Optimal Dispatch of Integrated Energy Systems Based on Stackelberg Game
by Fan Zhang, Jijing Yan, Yuxi Li and Ziwei Zhu
Technologies 2025, 13(12), 579; https://doi.org/10.3390/technologies13120579 - 10 Dec 2025
Viewed by 424
Abstract
As a key node of the energy internet, the park-level integrated energy system undertakes the dual functions of improving energy supply reliability and promoting low-carbon development in the transformation of the global energy structure. The need to simultaneously meet terminal energy demand and [...] Read more.
As a key node of the energy internet, the park-level integrated energy system undertakes the dual functions of improving energy supply reliability and promoting low-carbon development in the transformation of the global energy structure. The need to simultaneously meet terminal energy demand and market regulation requirements constrains operational optimization due to factors such as energy price fluctuations. Future research should focus on supply–demand coordination mechanisms and energy efficiency improvement strategies to advance the high-quality development of such systems. To this end, this study constructs a collaborative optimization framework integrating demand response based on a dual-compensation mechanism and dynamic multi-energy pricing and incorporates it into a Stackelberg game-based low-carbon economic dispatch model. By incorporating a dynamic multi-energy pricing mechanism, the model coordinates and optimizes the interests of the upper-level park integrated energy system operator (PIESO) and the lower-level park users. On the supply side, the model couples a two-stage power-to-gas (P2G) device with a stepwise carbon trading mechanism, forming a low-carbon dispatch system enabling source–grid–load coordination. On the demand side, an integrated demand response mechanism with dual compensation is introduced to enhance the coupling intensity of multi-energy flows and the adjustability of price elasticity. The simulation results show that, compared with traditional models, the proposed optimization framework achieves improvements in three dimensions: carbon emissions, economic benefits, and user costs. Specifically, the carbon emission intensity is reduced by 28.04%, the operating income of the PIESO is increased by 29.53%, and the users’ energy consumption cost is decreased by 13.05%, which verifies the effectiveness and superiority of the proposed model. Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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23 pages, 8144 KB  
Article
Carbon Emission Reduction Capability Analysis of Electricity–Hydrogen Integrated Energy Storage Systems
by Rankai Zhu, Yuxi Li, Xu Huang, Yaoxuan Xia, Yunjin Tu, Bowen Zheng, Jing Qiu and Xiaoshun Zhang
Technologies 2025, 13(10), 472; https://doi.org/10.3390/technologies13100472 - 18 Oct 2025
Viewed by 1068
Abstract
Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper [...] Read more.
Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs, focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL), a fuel cell (FC), hydrogen storage tanks, and battery energy storage was constructed. Three optimisation objectives—cost minimisation, carbon emission minimisation, and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability, environmental performance, and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction, providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen systems. Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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