Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88849
Title: | Multiobjective reinforcement learning-based intelligent approach for optimization of activation rules in automatic generation control | Authors: | Wang, HZ Lei, ZX Zhang, X Peng, JC Jiang, H |
Issue Date: | 2019 | Source: | IEEE access, 2019, , v. 7, p. 17480-17492 | Abstract: | This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids. | Keywords: | Activation rule Automatic generation control Multiobjective reinforcement learning Particle swarm optimization |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2019.2894756 | Rights: | © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. The following publication H. Wang, Z. Lei, X. Zhang, J. Peng and H. Jiang, "Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control," in IEEE Access, vol. 7, pp. 17480-17492, 2019, doi: 10.1109/ACCESS.2019.2894756. is available at https://dx.doi.org/10.1109/ACCESS.2019.2894756 Posted with permission of the publisher |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wang_Multiobjective_Reinforcement_Learning-Based.pdf | 8.01 MB | Adobe PDF | View/Open |
Page views
35
Last Week
0
0
Last month
Citations as of May 5, 2024
Downloads
15
Citations as of May 5, 2024
SCOPUSTM
Citations
23
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
20
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.