Analyzing Input Data Influence in Local Techno-Economic Analyses for Power-to-Methane Plants

Power-to-methane has been identified as a solution for quickly and effectively exploiting surplus electricity potential. Nevertheless, due to the current efficiencies, costs and sizes, it may not be suitable for local energy transitions. This article presents the results of two optimization models developed in Calliope for two case studies regions, with low wind levels in southern Germany with different electricity mixes. The optimization models simulate all available generation sources in the regions and their extensions with an additional Power-to-methane plant. The model minimizes the cost of the overall systems that meet the given demand including the gas, district heating, and electricity systems. Annual gas and district heating demand are generated based on collected data from industries, commerce, and households and standard load profiles, while annual electricity demand is obtained from public statistics. Besides, hourly electricity demand data from industries, commerce, and households are collected. Results are compared with scenarios using standard load profiles for electricity. The results show that the use of Power-to-methane is significantly affected by the load profiles used as well as the existing technological mix.