Deep Reinforcement Learning Control Strategy for Primary Frequency Regulation ofEnergy Storage Assisted Thermal Power Units

被引:0
|
作者
Wang J. [1 ]
Sun R. [1 ]
Liu Z. [2 ]
Zhang X. [3 ]
Guo H. [1 ]
Hu H. [2 ]
机构
[1] State Grid Henan Electric Power Company, Zhengzhou
[2] School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an
[3] Electric Power Research Institute of State Grid Henan Electric Power Company, Zhengzhou
来源
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2024年 / 58卷 / 06期
关键词
deep reinforcement learning; droop control; frequency dead band; primary frequency control; state of charge;
D O I
10.7652/xjtuxb202406017
中图分类号
学科分类号
摘要
In order to fully tap the potential of primary frequency modulation in the energy storage unit of the thermal-storage combined system and reduce the frequency regulation of thermal power units, a twin delayed deep deterministic policy gradient algorithm based energy storage assisted primary frequency regulation control strategy for thermal power unit is proposed. A typical regional power grid primary frequency regulation model containing thermal-storage combined frequency regulation system is established. Optimization problems with multiple objectives such as improving the frequency control effect of the power grid, maintaining a stable state of charge(SOC), and reducing the frequency regulation actions of thermal power unit are set. Taking into account the constraints of power grid operation status and frequency regulation actions, the primary frequency regulation problem of thermal power unit assisted by energy storage is modeled as a Markov decision process. Twin delayed deep deterministic policy gradient algorithm is used for optimization problem solving. The simulation results of typical frequency regulation task scenarios show that compared with traditional frequency regulation system control algorithms, the proposed energy storage assisted intelligent frequency regulation control strategy for thermal power units can reduce the average frequency difference of the power grid by about 10.4%, and reduce the frequency regulation actions of thermal power unit by about 56.2%. This verifies that the strategy can effectively release the frequency regulation potential of the thermal-storage combined system while significantly reducing frequency regulation of thermal power units. © 2024 Xi'an Jiaotong University. All rights reserved.
引用
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页码:186 / 192
页数:6
相关论文
共 25 条
  • [11] LI Xinran, CUI Xiwen, HUANG Jiyuan, Et al., The Self-Adaption control strategy of energy storage batteries participating in the primary frequency regulation, Transactions of China Electrotechnical Society, 34, 18, pp. 3897-3908, (2019)
  • [12] WU Qifan, SONG Xinli, ZHANG Jingran, Et al., Study on self-adaptation comprehensive strategy of battery energy storage in primary frequency regulation of power grid, Power System Technology, 44, 10, pp. 3829-3836, (2020)
  • [13] LI Junhui, HOU Tao, MU Gang, Et al., Primary frequency regulation strategy with energy storage system based on weight factors and state of charge recovery, Automation of Electric Power Systems, 44, 19, pp. 63-72, (2020)
  • [14] WANG Yufei, YANG Mingcheng, XUE Hua, Et al., Self-adaptive integrated control strategy of battery energy storage system considering SOC for primary frequency regulation [J], Electric Power Automation Equipment, 41, 10, pp. 192-198, (2021)
  • [15] LI Junhui, GAO Zhuo, YING Hong, Et al., Primary frequency regulation control strategy of energy storage based on dynamic droop coefficient and SOC reference, Power System Protection and Control, 49, 5, pp. 1-10, (2021)
  • [16] MA Zhihui, LI Xinran, TAN Zhuangxi, Et al., Integrated control of primary frequency regulation considering dead band of energy storage, Transactions of China Electrotechnical Society, 34, 10, pp. 2102-2115, (2019)
  • [17] ZHANG Feng, YOU Huanhuan, DING Lei, Influential mechanism modelling of dead band in primary frequency regulation of renewable energy and its coefficient correction strategy, Automation of Electric Power Systems, 47, 6, pp. 158-167, (2023)
  • [18] HU Junyan, NIU Hanlin, CARRASCO J, Et al., Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning, IEEE Transactions on Vehicular Technology, 69, 12, pp. 14413-14423, (2020)
  • [19] FUJIMOTO S, VAN HOOF H, MEGER D., Addressing function approximation error in actor-critic methods, Proceedings of the 35th International Conference on Machine Learning, pp. 1587-1596, (2018)
  • [20] LILLICRAP T P, HUNT J J, PRITZEL A, Et al., Continuous control with deep reinforcement learning