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Hybrid model predictive control for premixed natural gas engine as distributed generator

Author

Listed:
  • Gong, Qichangyi
  • Ye, Jie
  • Xu, Jinbang
  • Xiong, Wenyu
  • Feng, Han
  • Wang, Daocan
  • Shen, Anwen

Abstract

The access to electricity is increasing worldwide with a considerable growing rate per year. Unfortunately, traditional large fossil power technologies based on coal and oil lead to a major concern in environmental pollution, so the distributed power generation utilizing natural gas engine is taken into consideration. In this paper, a hybrid model predictive control (HMPC) strategy is proposed for natural gas engines in distributed power generation. The original fourth-order double-input double-output (DIDO) nonlinear engine model is transformed into two simpler models, one is a first-order model for air–fuel ratio (AFR) control, and a linear model predictive control (LMPC) strategy is designed. The other is a second-order nonlinear model for speed control, and the AFR is used as one of its two inputs to design nonlinear model predictive control (NMPC) strategy. The advantage of this is that each model is simpler than the original fourth-order model, and prediction horizon can be lengthened to achieve better control performance. For the optimization problem of NMPC, a switching mechanism strategy is proposed. It can effectively reduce the number of SQP iterations in both transient and steady-state conditions. Both simulations and experiments verify the feasibility of the HMPC and the effectiveness of the switching mechanism.

Suggested Citation

  • Gong, Qichangyi & Ye, Jie & Xu, Jinbang & Xiong, Wenyu & Feng, Han & Wang, Daocan & Shen, Anwen, 2023. "Hybrid model predictive control for premixed natural gas engine as distributed generator," Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:c:s0360544223011222
    DOI: 10.1016/j.energy.2023.127728
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    Cited by:

    1. Yuan, Ziyun & Chen, Lei & Liu, Gang & Zhang, Yuhan, 2023. "Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length," Energy, Elsevier, vol. 285(C).

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