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Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning

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  • Li, Jiawen
  • Yu, Tao
  • Zhang, Xiaoshun

Abstract

To dynamically balance multiple energy fluctuations in a multi-area integrated energy system (IES), a coordinated power control framework, named distributed intelligent coordinated automatic generation control (DIC-AGC), is constructed among different areas during load frequency control (LFC). Furthermore, an evolutionary imitation curriculum multi-agent deep deterministic policy gradient (EIC-MADDPG) algorithm is proposed as a novel deep reinforcement learning algorithm to realize coordinated control and improve the performance of DIC-AGC in the performance-based frequency regulation market. EIC-MADDPG, which combines imitation learning and curriculum learning, can adaptively derive the optimal coordinated control strategies for multiple areas of LFC controllers through centralized learning and decentralized implementation. The simulation of a four-area LFC-IES model on the China Southern Grid (CSG) demonstrates the effectiveness of the proposed method in maximizing control performance while minimizing regulation mileage payment in every area against stochastic load and renewable power fluctuations.

Suggested Citation

  • Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012137
    DOI: 10.1016/j.apenergy.2021.117900
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    References listed on IDEAS

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    1. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
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    4. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
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    Cited by:

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    2. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    3. Li, Zhihao & Yang, Lun & Xu, Yinliang, 2023. "A dynamics-constrained method for distributed frequency regulation in low-inertia power systems," Applied Energy, Elsevier, vol. 344(C).
    4. Mokhtar Aly & Emad A. Mohamed & Abdullah M. Noman & Emad M. Ahmed & Fayez F. M. El-Sousy & Masayuki Watanabe, 2023. "Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations," Mathematics, MDPI, vol. 11(9), pages 1-31, April.
    5. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    6. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    7. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Coordinated control of mHTGR-based nuclear steam supply systems considering cold helium temperature," Energy, Elsevier, vol. 284(C).
    8. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    9. Bhargav Appasani & Amitkumar V. Jha & Deepak Kumar Gupta & Nicu Bizon & Phatiphat Thounthong, 2023. "PSO α : A Fragmented Swarm Optimisation for Improved Load Frequency Control of a Hybrid Power System Using FOPID," Energies, MDPI, vol. 16(5), pages 1-17, February.
    10. Li, Jiawen & Zhou, Tao & Keke, He & Yu, Hengwen & Du, Hongwei & Liu, Shuangyu & Cui, Haoyang, 2023. "Distributed quantum multiagent deep meta reinforcement learning for area autonomy energy management of a multiarea microgrid," Applied Energy, Elsevier, vol. 343(C).
    11. Tengfei Weng & Yan Xie & Guorong Chen & Qi Han & Yuan Tian & Liping Feng & Yangjun Pei, 2022. "Load frequency control under false data inject attacks based on multi-agent system method in multi-area power systems," International Journal of Distributed Sensor Networks, , vol. 18(4), pages 15501329221, April.
    12. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    13. Xinghua Liu & Siwei Qiao & Zhiwei Liu, 2023. "A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies," Energies, MDPI, vol. 16(5), pages 1-22, February.
    14. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    15. Oshnoei, Arman & Kheradmandi, Morteza & Blaabjerg, Frede & Hatziargyriou, Nikos D. & Muyeen, S.M. & Anvari-Moghaddam, Amjad, 2022. "Coordinated control scheme for provision of frequency regulation service by virtual power plants," Applied Energy, Elsevier, vol. 325(C).

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