Combined Scheduling and Configuration Optimization of Power-to-Methanol System Considering Feedback Control of Thermal Power

Junjie Ye, Yinghui Liu, Li Sun () and Ke Chen
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Junjie Ye: Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique, Beijing 100192, China
Yinghui Liu: Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique, Beijing 100192, China
Li Sun: National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Liyang Research Institute of Southeast University, Liyang 213300, China
Ke Chen: Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique, Beijing 100192, China

Energies, 2025, vol. 18, issue 5, 1-20

Abstract: A power-to-methanol (P2M) system is a promising energy storage approach in transforming surplus renewable energy into a chemical product while utilizing the captured CO 2 from conventional thermal power units. Most of the traditional methods for the optimal configuration of IES use the steady-state model of the equipment, while ignoring the dynamic deviation of the thermal power unit under variable operating conditions. This study enhances the steady-state model of the P2M system by incorporating feedback-based dynamic control for the thermal power generation (TPG) unit. A closed-loop state-space model of the TPG unit is introduced as an additional constraint within the optimization framework. Furthermore, a dynamic deviation index for the TPG unit is formulated and integrated into a mixed-integer linear programming (MILP) model. Together with the system’s annual operating cost over its life cycle, this index constitutes an objective function, aiming to minimize both the dynamic deviations and operating costs, thereby optimizing the capacity configuration of the P2M system’s components. The optimal results indicate that in the dynamic configuration, the hydrogen storage tank capacity increases by 94.73% and the electrolyzer capacity remains almost consistent, which shows the energy storage potential of the P2M. The optimized scheduling results show that the electrolyzer can effectively absorb the intermittency of renewable energy. This method of dynamic configuration planning can effectively suppress the thermal power unit output fluctuation, smooth the schedule curve, and realize the effect of peak shaving and valley filling.

Keywords: power-to-methanol (P2M) system; energy storage; configuration and scheduling optimization; dynamic characterization; mixed-integer linear programming (MILP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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