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

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 CO2 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.