Carbon price fluctuations significantly affect the economics of methanol production. Conventional renewable and fossil fuel-based processes exhibit varied economic performance under different carbon prices and face economic risks when carbon prices fluctuate. This study proposes a carbon price-driven flexible process for methanol production. The system integrates green hydrogen production, carbon capture, methanol synthesis, and methane reforming, and dynamically switches between two operational modes: under higher carbon prices, it runs on renewable energy. However, under lower carbon prices, it operates based on fossil fuels. Monte Carlo simulations are conducted under three fluctuation amplitudes (10–20 /t) and standard deviation levels (10 %–20 % of the mean). The results show the flexible process performs better in all scenarios. Compared to the purely renewable route, it achieves revenue advantages of 10. 65 M and 11. 21 M at carbon price fluctuations of 15 /t and 20 /t, respectively. Across different standard deviation levels, the advantage expands to 23. 41–36. 39 M. The proposed process effectively improves both economic returns and risk resistance in methanol production. This study presents a flexible methanol production system that dynamically switches between green and fossil fuel-based routes based on carbon price signals. When carbon prices rise above a threshold, it produces green methanol using captured CO 2 and renewable hydrogen. This green mode boosts cash flow via carbon credit sales and green methanol premiums. If carbon prices fall below the threshold, the system shifts to methane-based production with carbon capture. In this mode, it cuts costs and sells surplus power to raise profits. Monte Carlo simulations show this scheduling strategy delivers higher net present value than single-route green or fossil methanol production. • A novel process scheduling strategy driven by carbon prices is proposed. • Carbon trading and electricity sales convert price risk into returns. • The carbon price threshold for operational mode switching is 144. 2/t. • Monte Carlo proves this scheduling yields highest NPV under volatility.
Pan et al. (Sun,) studied this question.