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PV/Hydrogen DC microgrid control using distributed economic model predictive control

Author

Listed:
  • Zhu, Zheng
  • Liu, Xiangjie
  • Kong, Xiaobing
  • Ma, Lele
  • Lee, Kwang Y.
  • Xu, Yuping

Abstract

The integration of hydrogen energy into a photovoltaic-dominated microgrid is now becoming a promising approach to improve the photoconversion efficiency and enhance the operating reliability. However, the energy management and power regulation of the Photovoltaic/Hydrogen DC microgrid face challenges due to the intermittency of photovoltaic (PV) power generation and the randomness of load. In this paper, a distributed economic model predictive control (DEMPC) scheme is developed for a PV/Hydrogen DC microgrid, which integrates the energy management, economic optimization, and power regulation into one optimal control framework. Based on the developed distributed converter-based mathematical model, three local controllers are designed to achieve the economic targets of PV subsystem, alkaline electrolyzer subsystem, and proton exchange membrane fuel cell subsystem respectively, which cooperate with each other through the communication network to realize the power supply–demand balance, DC bus voltage stability, and economic optimization. A mixed integer nonlinear programming algorithm utilizing the finite converter switching states is embedded into the DEMPC to solve the non-convex local optimization problems efficiently. The effectiveness and superiority of the proposed DEMPC scheme are verified by simulations under varying irradiance and load conditions, indicating that the DEMPC can achieve a comparable overall dynamic economic performance with a significantly reduced computation burden and power oscillation, compared to the centralized economic model predictive control (CEMPC).

Suggested Citation

  • Zhu, Zheng & Liu, Xiangjie & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y. & Xu, Yuping, 2024. "PV/Hydrogen DC microgrid control using distributed economic model predictive control," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s096014812301786x
    DOI: 10.1016/j.renene.2023.119871
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