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

Gong, Qichangyi; Ye, Jie; Xu, Jinbang; Xiong, Wei; Han, Feng; Wang, Daocan; Shen, Anwen

doi:10.1016/j.energy.2023.127728

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…For further development of the control speed and reduction in oscillation, advanced control methods on the engine speed and air-fuel ratio have been presented. Gong et al [154] designed a hybrid model predictive control strategy for improving the control performance of the gas engine transients. This strategy was used for a hybrid model containing a first-order model for air-fuel ratio control and a second-order nonlinear model for speed control.…”

Section: Figure 15mentioning

confidence: 99%

“…Gong et al [154] designed a hybrid model predictive control strategy for improving the control performance of the gas engine transients. This strategy was used for a hybrid model containing a first-order model for air-fuel ratio control and a second-order nonlinear model for speed control.…”

Section: Figure 15mentioning

confidence: 99%

“…Corresponding control strategies and advanced algorithms have been designed to achieve the high-precision transient air-fuel ratio and speed control [222,223]. Despite the similar simulation costs of nonlinear and linear models, concerning the convenience of matching with the controller, the nonlinear engine and the system dynamics functions can be linearized at specific points by the Tylor expansion [122,154]. Such models are not limited to the physical-based simulation tools, simulation software such as MATLAB/Simulink and Python have been used to build the systems.…”

Section: Model Fidelity and Transient Study-oriented Reductionmentioning

confidence: 99%

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Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

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Liquefied natural gas (LNG) is widely regarded as the midterm solution toward zero-carbon transportation at sea. However, further applications of gas engines are challenging due to their weak dynamic load performance. Therefore, the comprehension of and improvements in the dynamic performance of gas-engine-based power systems are necessary and urgent. A detailed review of research on mechanisms, modeling, and optimization is indispensable to summarize current studies and solutions. Developments in engine air-path systems and power system load control have been summarized and compared. Mechanism studies and modeling methods for engine dynamic performance were investigated and concluded considering the trade-off between precision and simulation cost. Beyond existing studies, this review provides insights into the challenges and potential pathways for future applications in decarbonization and energy diversification. For further utilization of clean fuels, like ammonia and hydrogen, the need for advanced air–fuel ratio control becomes apparent. These measures should be grounded in a deep understanding of current gas engines and the combustion characteristics of new fuels. Additionally, the inherent low inertia feature of electric power systems, and consequently the weak dynamic performance when adopting renewable energies, must be considered and studied to ensure system reliability and safety during transient conditions.

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Section: Figure 15mentioning

confidence: 99%

Section: Figure 15mentioning

confidence: 99%

Section: Model Fidelity and Transient Study-oriented Reductionmentioning

confidence: 99%

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et al. 2023

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Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length

Yuan,

Chen,

Liu

et al. 2023

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Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

Riedler,

Franz,

Kozek

et al. 2024

International Journal of Engine Research

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Gas engine generator sets are applied in microgrids due to their capability to provide reliable, responsive and efficient distributed power generation, enhancing grid resilience and enabling the integration of renewable energy sources. This article proposes a methodology for stabilizing the frequency of an isolated microgrid subjected to a measurable disturbance by controlling the power of a premixed turbocharged natural gas engine. Control difficulties of this task include strongly nonlinear dynamics, time delay in the air-fuel path and constrained operating conditions. Conventional control methods like proportional-integral control have difficulties solving these problems, and require extensive tuning efforts and gain scheduling to deliver acceptable performance, prompting adoption of advanced control strategies. The presented approach utilizes a cascaded control architecture with a nonlinear model predictive controller (NMPC) for high level control and engine specific low-level controllers for controlling the intake manifold pressure and air-fuel ratio. The NMPC captures the inherent nonlinear dynamics, accounts for an input delay and handles state-dependent constraints. The cascaded control architecture enables the usage of a comparably simple model for the NMPC design, reducing the computational cost and the parametrization effort. This additionally enables application of the high level controller for different gas engine types and allows design of the controller in a simple simulation environment prior to the experiments on the real engine. Experimental results highlight the NMPC’s ability to control the engine at its physical limits over the entire load range without inducing frequency oscillations using just one parameter set. This emphasizes the robustness of the presented approach, making it a promising solution for real-world applications.

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

Cited by 3 publications

References 33 publications

Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length

Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

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