(434b) Distributed Manufacturing for Electrified Chemical Processes in a Microgrid

Chemical industries are a major source of greenhouse gas emissions and are responsible for 7% of global greenhouse emissions (Tickner et al., 2021) as they are mostly powered by the combustion of fossil fuels. To solve this problem, the electrification of the chemical industry is a solution that is currently being explored by researchers across the world. Electrification helps decarbonize the chemical industry and move from fossil fuels to renewable energy sources such as solar and wind. Electrification can be done in different parts of the process including reaction and separation. A number of works have been done on electrification focusing on the optimization of process scheduling and design such as (Kelley et al., 2022), (Allman & Daoutidis, 2018), (Wang et al., 2021), (Sánchez & Martín, 2018), (He et al., 2021),(Guerra et al., 2019),(Demirhan et al., 2019)and (Cooper et al., 2022).

Renewable resources vary in their power output across different locations. A good way to take advantage of these variations is by implementing Distributed Chemical Manufacturing. Distributed Chemical Manufacturing (DCheM) aims to improve chemical process industries by developing modular process plants, which take advantage of distributed resources and/or address distributed environmental problems. DCheM paves way for the introduction of numerous new process technologies and simultaneously supports and enables energy and environmental sustainability while reducing transportation costs. Modular processes increase the flexibility in dealing with the variability of conditions. Work has been done in this field, such as (Sampat et al., 2021), (Lara & Grossmann, 2016), and (Palys et al., 2019).

While work has been done on the design and optimization of electrified chemical plants, one of the areas which can be explored more is the spatial features of such plants as renewable resources such as wind and solar vary with location. While some research has been done on the supply chain optimization of a particular set of chemicals produced by electricity such as (Ochoa Bique & Zondervan, 2018), (Welder et al., 2018) and (He et al., 2021), these models do not consider the monthly demand variability or in some cases the hourly variability. It is important to consider these temporal variations as renewable resources vary in their power output across different time scales. Another important consideration is combining the planning of power resources and chemical plants. The co-expansion of the chemical and electric network like (Li et al., 2022) is a less explored area. Thus, there is a need to model both temporal and spatial features across time scales for electrified chemical processes and integrate the planning of the power resources with it.

In this presentation, we address this gap, where we model a network of plants and power-generating units with three-time scales, single-time, monthly, and hourly. The problem is considered under the context of a microgrid, which typically consists of a network of low-voltage power generating units, storage devices, and loads capable of supplying a local area such as a suburban area, industry, or any commercial area with electric power and heat (Mahmoud et al., 2014). The objective of this research is to design a network to facilitate DCheM for electrified chemical processes with the power demand satisfied by renewable sources as well as power from a utility grid coordinated by a microgrid operator by using an MILP (Mixed Integer Linear Programming) model.

The proposed model encompasses three-time scales taking into account investment decisions, as well as monthly decisions and hourly decisions incorporating them into the same model and thus capturing temporal variations as well as spatial variations in all time scales. The model incorporates both the microgrid (generating units and transmission lines) and chemical plant expansion in a single model. Therefore, the tradeoffs between the transportation of chemicals and the transmission of power are considered. Further, the size of the model can easily exceed millions of variables. We develop a spatial aggregation and disaggregation algorithm based on k-means clustering to solve the model efficiently while maintaining a small optimality gap.

The model is tested using a case study with 20 candidate locations in Western Texas where we try to place chlor-alkali plants, solar panels, and wind turbines. The optimal solution can locate the chemical plants and power generating units such that the plants are operated to take advantage of the spatial and temporal variations of renewable output and electricity price. For the case study, the net annual revenue obtained using a model that ignores the temporal variations is about 18% less than the net annual revenue obtained from our multiscale model.

References

Allman, A., & Daoutidis, P. (2018). Optimal scheduling for wind-powered ammonia generation: Effects of key design parameters. Chemical Engineering Research and Design, 131, 5–15. https://doi.org/https://doi.org/10.1016/j.cherd.2017.10.010

Cooper, N., Horend, C., Röben, F., Bardow, A., & Shah, N. (2022). A framework for the design & operation of a large-scale wind-powered hydrogen electrolyzer hub. International Journal of Hydrogen Energy, 47(14), 8671–8686. https://doi.org/10.1016/J.IJHYDENE.2021.12.225

Demirhan, C. D., Tso, W. W., Powell, J. B., & Pistikopoulos, E. N. (2019). Sustainable ammonia production through process synthesis and global optimization. AIChE Journal, 65(7), e16498. https://doi.org/https://doi.org/10.1002/aic.16498

Guerra, O. J., Eichman, J., Kurtz, J., & Hodge, B. M. (2019). Cost Competitiveness of Electrolytic Hydrogen. Joule, 3(10), 2425–2443. https://doi.org/10.1016/J.JOULE.2019.07.006

He, G., Mallapragada, D. S., Bose, A., Heuberger, C. F., & Gençer, E. (2021). Hydrogen Supply Chain Planning With Flexible Transmission and Storage Scheduling. IEEE Transactions on Sustainable Energy, 12(3), 1730–1740. https://doi.org/10.1109/TSTE.2021.3064015

Kelley, M. T., Do, T. T., & Baldea, M. (2022). Evaluating the demand response potential of ammonia plants. AIChE Journal, 68(3), e17552. https://doi.org/https://doi.org/10.1002/aic.17552

Lara, C. L., & Grossmann, I. E. (2016). Global Optimization for a Continuous Location-Allocation Model for Centralized and Distributed Manufacturing. Computer Aided Chemical Engineering, 38, 1009–1014. https://doi.org/10.1016/B978-0-444-63428-3.50173-9

Li, J., Lin, J., Heuser, P.-M., Heinrichs, H. U., Xiao, J., Liu, F., Robinius, M., Song, Y., & Stolten, D. (2022). Co-Planning of Regional Wind Resources-based Ammonia Industry and the Electric Network: A Case Study of Inner Mongolia. IEEE Transactions on Power Systems, 37(1), 65–80. https://doi.org/10.1109/TPWRS.2021.3089365

Ochoa Bique, A., & Zondervan, E. (2018). An outlook towards hydrogen supply chain networks in 2050 — Design of novel fuel infrastructures in Germany. Chemical Engineering Research and Design, 134, 90–103. https://doi.org/10.1016/J.CHERD.2018.03.037

Palys, M. J., Allman, A., & Daoutidis, P. (2019). Exploring the Benefits of Modular Renewable-Powered Ammonia Production: A Supply Chain Optimization Study. Industrial & Engineering Chemistry Research, 58(15), 5898–5908. https://doi.org/10.1021/acs.iecr.8b04189

Sampat, A. M., Kumar, R., Pushpangatha Kurup, R., Chiu, K., Saucedo, V. M., & Zavala, V. M. (2021). Multisite supply planning for drug products under uncertainty. AIChE Journal, 67(1), e17069. https://doi.org/https://doi.org/10.1002/aic.17069

Sánchez, A., & Martín, M. (2018). Scale up and scale down issues of renewable ammonia plants: Towards modular design. Sustainable Production and Consumption, 16, 176–192. https://doi.org/10.1016/J.SPC.2018.08.001

Tickner, J., Geiser, K., & Baima, S. (2021). Transitioning the Chemical Industry: The Case for Addressing the Climate, Toxics, and Plastics Crises. Environment: Science and Policy for Sustainable Development, 63(6), 4–15. https://doi.org/10.1080/00139157.2021.1979857

Wang, H., Daoutidis, P., & Zhang, Q. (2021). Harnessing the Wind Power of the Ocean with Green Offshore Ammonia. ACS Sustainable Chemistry & Engineering, 9(43), 14605–14617. https://doi.org/10.1021/acssuschemeng.1c06030

Welder, L., Ryberg, D. S., Kotzur, L., Grube, T., Robinius, M., & Stolten, D. (2018). Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany. Energy, 158, 1130–1149. https://doi.org/10.1016/J.ENERGY.2018.05.059