IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8886-d867451.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8886/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8886/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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).
    4. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    5. 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).
    6. 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).
    7. 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.
    8. 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).
    9. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Sun, Qie & Fu, Yu & Lin, Haiyang & Wennersten, Ronald, 2022. "A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties," Applied Energy, Elsevier, vol. 314(C).
    3. Shahbazbegian, Vahid & Shafie-khah, Miadreza & Laaksonen, Hannu & Strbac, Goran & Ameli, Hossein, 2023. "Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems," Applied Energy, Elsevier, vol. 348(C).
    4. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    5. Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    6. Adlan Pradana & Mejbaul Haque & Mithulanathan Nadarajah, 2023. "Control Strategies of Electric Vehicles Participating in Ancillary Services: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-36, February.
    7. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.
    8. Wang, Yubao & Huang, Xiaozhou & Huang, Zhendong, 2024. "Energy-related uncertainty and Chinese stock market returns," Finance Research Letters, Elsevier, vol. 62(PB).
    9. Hunt, Julian David & Nascimento, Andreas & Zakeri, Behnam & Barbosa, Paulo Sérgio Franco, 2022. "Hydrogen Deep Ocean Link: a global sustainable interconnected energy grid," Energy, Elsevier, vol. 249(C).
    10. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    11. Liu, Xinglei & Liu, Jun & Ren, Kezheng & Liu, Xiaoming & Liu, Jiacheng, 2022. "An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision," Energy, Elsevier, vol. 261(PB).
    12. Muhammad Habib Ur Rehman & Luigi Coppola & Ernestino Lufrano & Isabella Nicotera & Cataldo Simari, 2023. "Enhancing Water Retention, Transport, and Conductivity Performance in Fuel Cell Applications: Nafion-Based Nanocomposite Membranes with Organomodified Graphene Oxide Nanoplatelets," Energies, MDPI, vol. 16(23), pages 1-11, November.
    13. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    14. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    15. Sung-Fu Hung & Aoni Xu & Xue Wang & Fengwang Li & Shao-Hui Hsu & Yuhang Li & Joshua Wicks & Eduardo González Cervantes & Armin Sedighian Rasouli & Yuguang C. Li & Mingchuan Luo & Dae-Hyun Nam & Ning W, 2022. "A metal-supported single-atom catalytic site enables carbon dioxide hydrogenation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    16. Zheng, Bobo & Xu, Jiuping & Ni, Ting & Li, Meihui, 2015. "Geothermal energy utilization trends from a technological paradigm perspective," Renewable Energy, Elsevier, vol. 77(C), pages 430-441.
    17. Mao, Guozhu & Zou, Hongyang & Chen, Guanyi & Du, Huibin & Zuo, Jian, 2015. "Past, current and future of biomass energy research: A bibliometric analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1823-1833.
    18. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    19. Luo, Rongrong & Wang, Liuwei & Yu, Wei & Shao, Feilong & Shen, Haikuo & Xie, Huaqing, 2023. "High energy storage density titanium nitride-pentaerythritol solid–solid composite phase change materials for light-thermal-electric conversion," Applied Energy, Elsevier, vol. 331(C).
    20. Ewa C. E. Rönnebro & Greg Whyatt & Michael Powell & Matthew Westman & Feng (Richard) Zheng & Zhigang Zak Fang, 2015. "Metal Hydrides for High-Temperature Power Generation," Energies, MDPI, vol. 8(8), pages 1-25, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8886-:d:867451. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.