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Robust Control Method for DC Microgrids and Energy Routers to Improve Voltage Stability in Energy Internet

Author

Listed:
  • Haochen Hua

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Yuchao Qin

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Hanxuan Xu

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Chuantong Hao

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Junwei Cao

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

Abstract

The energy internet (EI) is a wide area power network that efficiently combines new energy technology and information technology, resulting in bidirectional on-demand power transmission and rational utilization of distributed energy resources (DERs). Since the stability of local network is a prerequisite for the normal operation of the entire EI, the direct current (DC) bus voltage stabilization for each individual DC microgrid (MG) is a core issue. In this paper, the dynamics of the EI system is modeled with a continuous stochastic system, which simultaneously considers related time-varying delays and norm-bounded modeling uncertainty. Meanwhile, the voltage stabilization issue is converted into a robust H ∞ control problem solved via a linear matrix inequality approach. To avoid the situation of over-control, constraints are set in controllers. The problem of finding a balance between voltage regulation performance and constraints for the controllers was also extensively investigated. Finally, the efficacy of the proposed methods is evaluated with numerical simulations.

Suggested Citation

  • Haochen Hua & Yuchao Qin & Hanxuan Xu & Chuantong Hao & Junwei Cao, 2019. "Robust Control Method for DC Microgrids and Energy Routers to Improve Voltage Stability in Energy Internet," Energies, MDPI, vol. 12(9), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1622-:d:226829
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    References listed on IDEAS

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    1. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    2. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    3. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
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    Cited by:

    1. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of the Control of a Grid Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(7), pages 1-32, March.
    2. Marcel Nicola & Claudiu-Ionel Nicola, 2021. "Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers," Energies, MDPI, vol. 14(2), pages 1-25, January.

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