Industry Transformation
The transformation of energy-intensive industrial activity from a reference year (for example 2019) to CO2 neutrality by a goal year (e.g. 2050) can be investigated with the help of energy system optimization. Our methodology accounts for future regional requirements through the implementation of district models and enables a detailed modeling of local industrial sites. In addition, European sector-coupled models are used by our group to integrate electricity market models into the optimization.
Transformation paths can be calculated by long-term structural optimization. This involves modeling expansion planning over several investment periods based on the reference year. Each investment period is resolved on an hourly basis to account for weather-dependent renewable energy. In addition to the energy requirements of electricity and steam at different temperature levels, material requirements such as basic chemicals and refinery products, as well as the resulting CO2 emissions, are being taken into account. Therefore, couplings to other economic and energy sectors and renewable energy production are integrated within the district model. The electricity and steam generators, storage facilities, and synthesis routes considered to meet the demand are characterized based on their techno-economic parameters and accounted for by the expansion planning. In addition, the potential for renewable energies and sustainable input materials are incorporated.
The long-term structural optimization allows for decision-making support on future investment decisions and matches both site-specific and higher-level infrastructure, such as electricity or hydrogen distribution networks. Core results include expansion capacities to be aimed for per investment period and the associated operational planning. In addition, future utilization conflicts can also be discussed by limiting the available potential of certain energy carriers.
Scenarios are used to derive the effects of certain parameters on the investment decisions to be made locally. This allows system-critical parameters to be identified and recommendations for action to be derived.