(661f) A Dynamic Optimization Model for Minimizing the Cost of Low-Carbon Industrial Clusters

Minimizing the cost of climate action is essential to ensure the economic feasibility of sustainable development. The literature in the field of optimization and process integration provides a variety of methods that allow the cost minimization of the different available CO₂ reduction options. Such methods generally focus on one of the major pathways, such as energy systems (ES) or CO₂ capture, utilization, and storage (CCUS), and provide optimal systems consisting of a restricted set of considered technologies. Providing an integrated strategy for CO₂ reduction requires a more comprehensive consideration of the different available options which allows the investigation of the synergies existing between the major pathways. This work provides a mixed integer linear programming (MILP) model which considers the dynamics of energy sources, products demand, and prices to optimize the sizing and operation of the different plants in an industrial cluster for a given level of emissions allowance. The model considers the integration of power, CO₂, and hydrogen, as well as energy storage, hydrogen storage, and CCUS. This integrated approach allows the consideration of the effect of the dynamics on the levelized costs of electricity, hydrogen, and CO₂ capture, which have major impacts on the profit margins of the considered products. A case study is presented to demonstrate the proposed method is screening for cost-optimal energy mix, hydrogen production pathways, hydrogen utilization opportunities, and CO₂ integration network, given a set level of allowed emissions. The case study considers renewable and fossil-based energy sources, different hydrogen production pathways (grey, green, and blue), and different production plants. The hydrogen and CO₂ integration networks account for the distances between the sources and sinks to determine the optimal allocations.