Electrification is increasingly being recognized as a key strategy for decarbonizing the chemical industry. As electrification efforts intensify in the coming years, we will see a significant increase in the number of chemical processes that consume large amounts of, preferably renewable, electricity. Given the intermittency in the availability and pricing of electricity, to remain cost-competitive, the chemical industry will require a paradigm shift from mostly steady-state toward highly dynamic process operation. To this end, we develop computational decision-support tools that can help chemical processes fully leverage their operational flexibility and take advantage of new demand response opportunities.
In the first part of this talk, we discuss how a power-intensive chemical process can be scheduled to optimally participate in both the energy and reserve markets. The key challenge lies the uncertainty that one does not know in advance when load reduction will be requested by the grid operator; we propose to address this problem using a robust optimization approach. In the second part, we consider a network of chemical processes that are owned and operated by different companies or stakeholders. We apply a distributed optimization framework that enables coordination while respecting local objectives and sharing minimum amount of information among the stakeholders. Results from our case studies indicate substantial improvements in overall and individual performances, and further demonstrate the ability of coordinated demand response to benefit the chemical industry as a whole, not only those processes that are large electricity consumers.