Integrated Spatially Explicit Optimization and Analysis of Cellulosic Bioenergy Landscapes and Supply Chains

Abstract

A meaningful scale renewable energy transition is not possible without large-scale efficiently designed and operated supply chains (SC). Unlike traditional manufacturing, renewable energy SCs are highly distributed spatially and temporally, but advanced modeling techniques can capture important system characteristics. This dissertation focuses on cellulosic biofuels. Because cellulosic feedstocks have yet to be planted and biorefineries have yet to be constructed, mathematical programming models can help to simultaneously optimize the strategic design and tactical operation of both the landscape and the SC. Landscape design, deciding where to plant bioenergy crops and how manage them (e.g. fertilization), has been shown to improve the environmental impact of biomass production (including soil carbon sequestration), but is studied largely separately from biofuel SC design. The novelty of this work is threefold. First, this work presents a stochastic mixed-integer programming model for cellulosic biofuel SCs and discusses insights into landscape and SC interactions that can improve system-wide economic and environmental performance. The stochastic solutions that are found outperform deterministic solutions, accounting for biomass yield uncertainty by planting more biomass than needed to meet biofuel demand, hedging against years with poor yields, and smoothing out variations in system cost. Second, model extensions enable the study of larger-scale systems considering carbon capture and storage, and analysis provides insight into interactions between the landscape and optimal technology portfolios at biorefineries, showing that the valuation of greenhouse gas (GHG) emissions, biofuel demand, and distribution of bioenergy lands influence which technologies are installed at certain locations. Finally, analysis of the definitions used to identify bioenergy lands in a SC context shows soil carbon sequestration plays a critical role when siting biorefineries and biomass using field-scale landscape design. Furthermore, land quality, quantity, and distribution interact with GHG mitigation targets, dictating the limits of the bioenergy landscape; however despite these limitations, significant GHG mitigation is possible for only a small increase in costs. The data processing strategies, mathematical programming models, and analyses of high-resolution large scale systems may provide decision makers with a better understanding of cellulosic biofuel SC and landscape interactions, leading to systems with attractive environmental and economic performance.