As one of the important Power to X technologies, CO2 methanation realizes the storage of green hydrogen and the emission reduction of flue gas CO2. In this study, process modeling was applied for CO2 methanation coupled with CO2 capture and water electrolysis. Aspen Plus and HYSYS models of 400 kW water electrolysis were set up for integration. The heat exchange network and the reaction temperature and catalyst usage of two methanation reactors are optimized. The effects of step-wise evolution of H2 flow on stream flows and reactors are also studied. The results show a system energy efficiency of 42.3% using a high-temperature solid oxide electrolysis cell (SOEC) and 30% monoethanolamide solution (MEA). The heat-exchange network significantly improves the efficiency to 61.5%. Optimizing two-stage methanation reactors realizes 98% CO2 conversion at 10018 h-1 gas hourly space velocity. The optimized reaction temperatures are 390 and 325 °C, respectively. The Aspen HYSYS dynamic modeling shows that the flows reach stability within 10 min under 1 kmol/h step-wise evolution of H2 flow. Longer time is required to approach the stability of the methanation reactor. Sharp evolutions of H2 flow cause higher amplitudes and a longer time to reach stability. The H2 flow lower than 35% of the initial value would result in the unstable behavior of the methanation reactor.

CO2 methanation shows advantages in Power-to-Gas technologies using renewable energy. In this article, Sm doped Ce x Sm y Zr1−x−y O2-δ complex is prepared using a one-pot hydrolysis method and is used as support for the Ru-Ni catalyst. The CO2 methanation activity at high temperature, GHSV, and thermal stability is tested. The results show that Sm doping is unfavorable for low-temperature methanation activity, but improves the thermal stability with Sm doping amount of 10 mol%. The Sm doping significantly enhances the performance at high temperatures (>400 °C ) and high GHSV (> 10000 h−1). Five reaction rate equations with different adsorption expressions are applied for kinetic analysis. Low activation energies ranging between 50.1 kJ/mol and 53.2 kJ/mol are observed for different models, which are lower than reported Ni-based catalysts. The equation considering both CO2 and H2O adsorption achieves the best-fitting results.

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In order to fully mobilize user-side resources in an increasingly open energy trading market, this paper proposes an optimal allocation strategy for electricity/heat/gas shared energy storage based on the probability prediction method. The proposed optimized configuration establishes an energy hub structure with electricity/heat/gas shared energy storage, and a twobody optimized model with two-layer from the view of users and providers participating in the shared energy storage business model is established. The bottom layer describes the uncertainty of new energy output based on the probability prediction method based on long-term and short-term memory and Bayesian neural network, a user-side shared energy storage charging and discharging model, which is optimized aiming to minimize the user's total cost, is established, and the decision information will be informed to the shared energy storage provider. At the top level, aiming to minimize the investment and construction cost of shared energy storage providers, concentrates on optimizing the allocation of energy storage power and capacity of decision-making entities. The big M method is adopted to relax and linearize the nonlinear part of the objective and constraints, and then it is transformed into a mixed-integer linear optimization problem. Finally, three typical application scenarios are established. As to the verification of the superiority of the strategy, the CPLEX optimization solver is called through the YALMIP toolbox in Matlab to solve the models in different scenarios, and the overall costs and benefits are jointly compared. From the case analysis, we can draw the conclusion that compared with the traditional buying and selling model, the shared energy storage business model in this paper effectively reduces the investment and construction scale of user-side energy storage, correspondingly reducing the investment and construction cost of user-built energy storage and the time cost of operating and maintaining physical energy storage.