(625g) Incentivizing Carbon Capture at Cellulosic Biorefineries: Integrated Optimization of Spatially Explicit Landscapes, Supply Chains, and Technology Portfolios

Cellulosic biomass has long been recognized as an important energy feedstock to help reduce reliance on fossil fuels and mitigate the greenhouse gas emissions from liquid fuels. Many technologies have been developed that convert cellulosic biomass into useful products but the economic and environmental tradeoffs between the technologies and their outputs make it unclear which is preferred or what an optimal portfolio of biorefinery technologies may be. Additionally, carbon capture and storage (CCS) technologies installed at the biorefinery further mitigate the GHG impact of producing biofuels but the incentive to install CCS technologies is sensitive to the credit received for the carbon that is captured. When choosing technologies to produce biofuels, the distributed nature of biomass feedstock must be considered. The uneven spatial distribution of biomass, the differences in price and GHG impact between electricity grids, and the cost to transport captured CO2 motivate studying biorefinery technology tradeoffs (including CCS) and biomass supply chains simultaneously.
In this work we introduce a spatially explicit mixed-integer linear programming model that considers the upstream design of the biomass landscape (crop establishment and fertilization), the supply chain network design (including inventory, transportation, and preprocessing depot and biorefinery location and capacity) and technology selection at the biorefinery with CCS from a wide array of options. With this flexible optimization model, we study the top-down design of a bioenergy supply chain in the USA Midwest to meet liquid fuel demand. We show that the optimal technology portfolio is sensitive to the carbon credit imposed, and at sufficiently high credits a net negative GHG balance can be achieved for reasonable increases in total system costs. Furthermore, we show that the spatially explicit supply chain model locates refineries strategically to balance the tradeoffs between areas of high biomass concentration and regions with favorable electricity price/GHG impact.