Optimizing Electrified Chemical Process Scheduling with Future Wholesale Electricity Pricing

Graduate Thesis Or Dissertation

Citations

Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/12579t472

Abstract

The chemical manufacturing industry amounts to 9.4% of all energy consumption in the United States, most of which comes from combusting fossil fuels, a high emissions process. One method of reducing emissions from this industry is electrifying its loads. Especially in regions where our power grid is increasing its penetrations of zero-marginal cost resources, it is expected to see declining, but more volatile, electricity prices. To allow chemical manufacturing plants to capitalize on these volatile electricity prices, this thesis builds a mixed integer linear programming optimization model for batch chemical manufacturing process scheduling. This thesis incorporates wholesale electricity rates into the plant’s operating costs and then maximizes the plant’s profit, allowing the model to choose the plant’s yield based on operational constraints. By using projected electricity pricing and emissions data for the years 2020, 2030, 2040 and 2050, the model shows that future grid volatility increases the cost savings and emissions reductions that scheduling models can attain. Using a case study of a fully electrified steam production process powered with electric boilers, this model shows an annual reduction in electricity costs of $58 million and reduced emissions of 23 million kg of CO2 in 2050, a significant motivator for plant owners and climate advocates alike.

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