Special Issues

AI Based Planning, Dispatching, and Operation of New Energy Systems

Submission Deadline: 10 January 2022 (closed) View: 7

Guest Editors

Bo Yang, Kunming University of Science and Technology, China. Email: yangbo_ac@outlook.com  
Junhui Li, Northeast Electric Power University, China. Email: lijunhui@neepu.edu.cn  
Yiyan Sang, Shanghai University of Electric Power, China. Email: ethanyys@outlook.com  
Yixuan Chen, The University of Hong Kong, China. Email: yxchen@eee.hku.hk  
Yaxing Ren, University of Warwick, United Kingdom. Email: yaxing.ren@warwick.ac.uk


With large-scale utilization and widespread integration of various renewable energy (e.g., wind and solar energies) into modern power systems, traditional power system that mainly consists of fossil energy has experienced dramatical transformation. The proposed "New Power System" aims to construct a clean, low carbon, safe, and efficient modern energy system, which takes the renewable energy as the basis while other fossil energy as the supplementary. Meanwhile, with the maturity of deep learning, the improvement of computing power, and the wealth of big data accumulated in the Internet era, artificial intelligence technology has achieved a dramatical development and widespread applications in power systems over the last decade.

With large-scale renewable energy integrated into modern power systems, power generation output cannot be controlled on demand due to its power generation has strong randomness and uncertainties. At the same time, after the integration of massive distributed renewable energy, the accuracy of power load forecasting is also greatly reduced, which means that both the generation side and the consumption side have highly random fluctuations, which will bring great challenges to the balance adjustment and flexible operation of power system. Besides, the stability and efficiency during generation, transmission, distribution, and utilization of power system will also be fundamentally changed. Therefore, exploitation and application of various advanced artificial intelligence techniques to deal with planning, dispatching, and operation of new energy systems are imperative.

The aim of this Special Issue is to pursue first-class research along this direction, focusing on the most recent investigations and studies on planning, dispatching, and operation strategies for new power system based on artificial intelligence techniques. The targeted readers include both academic researchers and industry professionals. Researchers and experts worldwide are invited to submit high-quality original research papers and review articles on the subsequent potential topics. 

Potential topics include but are not limited to the following:

• Planning of renewable energy system 

• Operation planning and control of energy storage system

• Life cycle perception and analysis

• Fault diagnosis of electrical equipment

• Big data and AI based user portrait construction technology

• Cloud computing 

• Power demand response and economic dispatching algorithm

• Regulation technique for power system intelligent power consumption

• Power market


Renewable energy, Energy storage, Power market, Regulation and control, Fault diagnosis, Artificial intelligence, Big data, Cloud computing.

Published Papers

  • Open Access


    Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

    Xun Zhang, Wanrong Bai, Haoyang Cui
    Energy Engineering, Vol.120, No.3, pp. 665-679, 2023, DOI:10.32604/ee.2023.020342
    (This article belongs to the Special Issue: AI Based Planning, Dispatching, and Operation of New Energy Systems)
    Abstract Optical Character Recognition (OCR) refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image. This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence (AI) algorithms, in which the different AI algorithms for OCR analysis are classified and reviewed. Firstly, the mechanisms and characteristics of artificial neural network-based OCR are summarized. Secondly, this paper explores machine learning-based OCR, and draws the conclusion that the algorithms available for this form of OCR are still in their infancy, with low generalization and More >

  • Open Access


    An Improved Biometric Fuzzy Signature with Timestamp of Blockchain Technology for Electrical Equipment Maintenance

    Rao Fu, Liming Wang, Xuesong Huo, Pei Pei, Haitao Jiang, Zhongxing Fu
    Energy Engineering, Vol.119, No.6, pp. 2621-2636, 2022, DOI:10.32604/ee.2022.020873
    (This article belongs to the Special Issue: AI Based Planning, Dispatching, and Operation of New Energy Systems)
    Abstract The power infrastructure of the power system is massive in size and dispersed throughout the system. Therefore, how to protect the information security in the operation and maintenance of power equipment is a difficult problem. This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance. Compared with previous blockchain transactions, the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance. Finally, the effectiveness of the proposed method More >

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