Carbon capture and storage will be necessary for some industries to reach carbon neutrality. One of the main associated challenges is the design of the network linking the CO ₂ sources to the storage sites. Establishing a CO ₂ network can be impacted by many uncertainties such as CO ₂ amounts, pipeline routes and the locations of emitters and carbon sinks. We present a framework to investigate different scenarios of a future CO ₂ network in Germany. The analyses compare the routes and associated costs of different scenarios. The developed model uses several geospatial datasets and an optimization scheme to yield realistic and cost-efficient outcomes. Parameters such as population density and existing infrastructure are integrated to calculate potential routes, which are then used as an input for the developed heuristic model to determine the optimum network. The derived framework is flexible and can be used for investigating other scenarios, regions and settings. The results show that the different scenarios have a profound impact on the optimal layout and costs. The investment costs of the investigated scenarios range between 1.3 and 3 billion EUR. The outcomes are important for academia, industry and policymaking for the ongoing discussions regarding the development of carbon infrastructure.

Large-scale energy storage plants based on power-to-gas-to-power (PtG–GtP) technologies incorporating high temperature electrolysis, catalytic methanation for the provision of synthetic natural gas (SNG) and novel, highly efficient SNG-fired Allam reconversion cycles allow for a confined and circular use of CO2/CH4 and thus an emission-free storage of intermittent renewable energy. This study features a thorough technology assessment for large-scale PtG–GtP storage plants based on highly efficient sCO2 power cycles combined with subsurface CO2 storage. The Allam cycle employs supercritical CO2 as working fluid as well as an oxy-combustion process to reach high efficiencies of up to 66%. The entire PtG–GtP process chain assessed in this study is expected to reach maximum roundtrip efficiencies of 54.2% (with dedicated and sufficient O2 storage) or 49.0% (with a dedicated air separation unit). The implementation of said energy storage systems into existing national energy grids will pose a major challenge, since they will require far-reaching infrastructural changes to the respective systems, such as extensive installations of renewable generation and electrolysis capacities as well as sufficient subsurface storage capacities for both CO2 and CH4. Therefore, this study incorporates an assessment of the present subsurface storage potential for CO2 and CH4 in Germany. Furthermore, a basic forecast study for the German energy system with an assumed mass deployment of the proposed SNG-based PtG–GtP energy storage system for the year 2050 is conducted. In case of a fully circular use of CO2/CH4, when electricity is solely generated by renewable energy sources, 736 GW of renewables, 234 GW of electrolysis and 62 GW of gas-to-power capacities are required in the best case scenario in 2050. The total storage volume on the national scale of Germany for both CO2 and CH4 was determined to be 7.8 billion N m3, respectively, leading to a CH4 storage capacity of 54.5 TW h. The presented investigations illustrate the feasibility of large-scale energy storage systems for renewable electricity based on high temperature electrolysis, catalytic methanation and Allam power cycles paired with large subsurface storages for CO2 and CH4.

Designing low-cost network layouts is an essential step in planning linked infrastructure. For the case of capacitated trees, such as oil or gas pipeline networks, the cost is usually a function of both pipeline diameter (i.e. ability to carry flow or transferred capacity) and pipeline length. Even for the case of incompressible, steady flow, minimizing cost becomes particularly difficult as network topology itself dictates local flow material balances, rendering the optimization space non-linear. The combinatorial nature of potential trees requires the use of graph optimization heuristics to achieve good solutions in reasonable time. In this work we perform a comparison of known literature network optimization heuristics and metaheuristics for finding minimum-cost capacitated trees without Steiner nodes, and propose novel algorithms, including a metaheuristic based on transferring edges of high valency nodes. Our metaheuristic achieves performance above similar algorithms studied, especially for larger graphs, usually producing a significantly higher proportion of optimal solutions, while remaining in line with time-complexity of algorithms found in the literature. Data points for graph node positions and capacities are first randomly generated, and secondly obtained from the German emissions trading CO2 source registry. As political will for applications and storage for hard-to-abate industry CO2 emissions is growing, efficient network design methods become relevant for new large-scale CO2 pipeline networks.