Electrification and decarbonization of the chemical industry has been underway and the major driver of this change is the shifting energy mix from fossil fuels to renewable sources (Deloitte, 2020). Use of renewable sources of energy for electricity production has been increasing in the past years, with traditional fuels like coal now being used less than renewables (U.S. Energy Information Administration, 2020) and natural gas production forecasted to decline (U.S. Energy Information Administration, 2022a). However, this shift to renewables has not eliminated significant problems that impact their efficiency and optimal utilization. Wind and solar power generation may vary significantly, even during the same day, depending on the location (U.S. Energy Information Administration, 2022b). That can lead to high power output when there is low demand or low output at peak energy demands. Furthermore, prime locations for wind or solar power generation may be located far from residential areas and have limited transmission capacities. Therefore, more efficient use of renewable energy can be enabled with better energy storage and increased local use of electricity.

In this work we aim to identify optimal ways of more efficiently using wind-generated power through integration with chemical manufacturing. In previous work (Giannikopoulos, et al., 2022) we focused on a small manufacturing network involving a shale gas processing plant, a wind farm and an ethane cracker facility. In this paper, we expand on this concept by using a larger supply chain superstructure, involving chemical manufacturing processes that can use natural gas and/or natural gas liquids as raw materials (e.g., processes for propylene and acrylic acid), and that are candidates for efficient integration with renewable energy. The network of gas processing, wind energy and chemical manufacturing is optimized to determine the best selection and configuration of the technology represented in the supply chain superstructure. Additional variables considered include the size of the wind farm, sales to the electrical grid, and the usage of various energy storage technologies (e.g., batteries, compressed air, hydrogen). Multi-objective optimization analysis is used, with objectives of profit and CO₂ emissions.

Acknowledgement: This paper is based upon work supported primarily by the National Science Foundation under Cooperative Agreement No. EEC-1647722 (CISTAR: NSF Engineering Research Center for Innovative and Strategic Transformation of Alkane Resources). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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