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A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG

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  • Peixiao Fan

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jia Hu

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430072, China)

  • Song Ke

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Yuxin Wen

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Shaobo Yang

    (Electric Power Research Institute of State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050011, China)

  • Jun Yang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

With the development of micro gas turbines (MT) and power-to-gas (P2G) technology, the electric–gas system plays an important role in maintaining the stable, economical, and flexible operation of the microgrid. When subjected to power load disturbance and natural gas load disturbance, the system controller needs to coordinately control the frequency of the microgrid and the gas pressure at the natural gas pipeline nodes. Additionally, the reliability and stability of a multi-microgrid system are much higher than that of a single microgrid, but its control technology is more complicated. Thus, a frequency–pressure cooperative control strategy of a multi-microgrid oriented to an electric–gas system is proposed in this paper. Firstly, based on the analysis of the operating characteristics of the natural gas network and the coupling equipment, the dynamic model of natural gas transmission is built. Secondly, a multi-microgrid load frequency control model including MT, P2G equipment, electric vehicles (EVs), distributed power sources and loads has been established. In addition, according to the three control objectives of microgrid frequency, node pressure and system coordination and stability, the structure of a Muti-Agent Deep Deterministic Policy Gradient (MADDPG) controller is designed, then the definition of space and reward functions are completed. Finally, different cases are set up in the multi-microgrid, and the simulation results are compared with PI control and fuzzy control. The simulation results show that, the proposed MADDPG controller can greatly suppress the frequency deviation caused by wind power and load disturbances and the air pressure fluctuations caused by natural gas network load fluctuations. Additionally, it can coordinate well the overall stability between the sub-microgrids of multi-microgrid.

Suggested Citation

  • Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8886-:d:867451
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    References listed on IDEAS

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    1. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    2. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    3. Xin Wang & Jun Yang & Lei Chen & Jifeng He, 2017. "Application of Liquid Hydrogen with SMES for Efficient Use of Renewable Energy in the Energy Internet," Energies, MDPI, vol. 10(2), pages 1-20, February.
    4. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    5. Jun Yang & Zhili Zeng & Yufei Tang & Jun Yan & Haibo He & Yunliang Wu, 2015. "Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory," Energies, MDPI, vol. 8(3), pages 1-20, March.
    6. Qin, Guangyu & Zhang, Meijuan & Yan, Qingyou & Xu, Chuanbo & Kammen, Daniel M., 2021. "Comprehensive evaluation of regional energy internet using a fuzzy analytic hierarchy process based on cloud model: A case in China," Energy, Elsevier, vol. 228(C).
    7. Li, Chenghao & Zheng, Siyang & Chen, Yufeng & Zeng, Zhiyong, 2021. "Proposal and parametric analysis of an innovative natural gas pressure reduction and liquefaction system for efficient exergy recovery and LNG storage," Energy, Elsevier, vol. 223(C).
    8. Salehi, Javad & Namvar, Amin & Gazijahani, Farhad Samadi & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction," Energy, Elsevier, vol. 240(C).
    9. Rao, Yingqing & Yang, Jun & Xiao, Jinxing & Xu, Bingyan & Liu, Wenjing & Li, Yonghui, 2021. "A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control," Energy, Elsevier, vol. 222(C).
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