A bottom-up model for assessing the transition of chemical industry parks towards carbon neutrality

The transition of the chemical industry toward carbon neutrality brings complex challenges and can be highly individual at the production-site level. Building on the well-known PyPSA framework, this study presents a linear programming expansion planning model spanning from 2025 to 2050. The model optimizes the energy supply infrastructure by integrating steam-generating technologies, renewable energy sources, and storage technologies. To deepen the understanding of how exogenous modeling assumptions influence the expansion results, electricity price contracts and planning foresight are investigated. Integration of renewable energy sources buffers electricity price volatility, whereas their exclusion leads to higher redundancy of fossil-based technologies. Achieving carbon neutrality necessitates the introduction of electricity price subsidies beyond 2040. Three foresight strategies — perfect, myopic, and rolling horizon — are compared, revealing significant impacts on investment decisions. Especially, technologies that experience a drastic reduction in investment costs or that are not available from the outset of the planning horizon may cause misinvestments. It was found that total system costs may vary by up to 20.35 % depending on the investigated scenario. Finally, a novel two-step global sensitivity analysis using the Morris screening method is conducted to identify the most influential input parameters. The results emphasize the importance of loads and energy carrier costs. By carrying out the global sensitivity analysis with limited foresight, the computational time was decreased by over 90 % while maintaining its information value. The expansion results highlight the importance of direct electrification and strategic foresight for achieving a cost-effective transition.