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Volume 10 Issue 4
Apr.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(4): 839-858
Z. J. Wang, W. Wei, J. Z. F. Pang, F. Liu, B. Yang, X. P. Guan, and S. W. Mei, “Online optimization in power systems with high penetration of renewable generation: Advances and prospects,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 839–858, Apr. 2023. doi: 10.1109/JAS.2023.123126
Citation: Z. J. Wang, W. Wei, J. Z. F. Pang, F. Liu, B. Yang, X. P. Guan, and S. W. Mei, “Online optimization in power systems with high penetration of renewable generation: Advances and prospects,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 839–858, Apr. 2023. doi: 10.1109/JAS.2023.123126
shu

Online Optimization in Power Systems With High Penetration of Renewable Generation: Advances and Prospects

doi: 10.1109/JAS.2023.123126
  • 1.

    Key Laboratory of System Control and Information Processing, Ministry of Education of China, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

  • 2.

    State Key Laboratory of Power System and the Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

  • 3.

    Institute of High Performance Computing (IHPC), Agency for Science Technology and Research (A*STAR), Singapore 138632, Singapore

Funds:  This work was supported by the National Natural Science Foundation of China (62103265), the “ChenGuang Program” Supported by the Shanghai Education Development Foundation and Shanghai Municipal Education Commission of China (20CG11), and the Young Elite Scientists Sponsorship Program by Cast of China Association for Science and Technology
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    Abstract
  • Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redirected attention to online optimization methods. However, online optimization is a broad topic that can be applied in and motivated by different settings, operated on different time scales, and built on different theoretical foundations. This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used. In particular, we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain, i.e., optimization-guided dynamic control, feedback optimization for single-period problems, Lyapunov-based optimization, and online convex optimization techniques for multi-period problems. Lastly, we recommend some potential future directions for online optimization in the power systems domain.

     

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    Highlights

    • The motivations for the use of online optimization within the power system are explained explicitly
    • Online optimization problems are classified into three main classes, largely defined by their time scales, namely, dynamic control, single-period optimization, and multi-period optimization. This will eliminate the long-existing confusion in this field and lower the barrier of entry for researchers to work on online optimization in power systems
    • A comprehensive review and a comparative analysis of four types of online optimization methods are performed, i.e., optimization-guided dynamic control, feedback optimization, Lyapunov optimization, and online convex optimization, covering their motivation, time scale, theoretical foundations, and typical applications
    • Critical challenges and promising directions are discussed for online decision-making in power systems in depth

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