A high-resolution bioenergy sector optimization model for Brazil

Abstract

Decarbonizing Brazil is crucial for reducing global greenhouse gas emissions. To that end, the Net-Zero Brazil (NZB) modeling study aims to provide viable pathways for the country to achieve net-zero emissions by 2050. The modeling will be done with unprecedented spatial, technological, and temporal resolution. It relies on a least-cost, multi-sector optimization model being developed by the Princeton ZERO Lab called MACRO. Given Brazil’s prominence in biofuels and land-use challenges, a strategic approach to bioenergy deployment is essential. My study presents a high-resolution bioenergy supply chain optimization model, Downscale, designed to integrate into NZB to determine cost-effective bioenergy production, processing, and distribution pathways at fine spatial, temporal, and technological resolutions. A key feature is its downscaling capability, which enhances MACRO by translating state-level energy system results into actionable strategies for local deployment. Downscale is a mixed-integer linear programming model that optimally locates bioenergy crops, conversion facilities, and transportation while incorporating economic, environmental, and land-use constraints within municipalities. It is a myopic optimization model with no look-ahead, called at every time step of MACRO optimization. Cost-supply data for biomass resources and techno-economic characteristics of a portfolio of conversion technologies were gathered at the municipality-level (5570 municipalities in Brazil) for future use in MACRO via state-level aggregation. Downscale was then tested for Mato Grosso do Sul, a key biofuel-producing state. Four scenarios were analyzed: uniform demand growth for bio-derived energy carriers, low environmental protection (allowing bioenergy crop production in the Pantanal region), modest electrification of energy demands, and high electrification of demands. Results indicate that strategic infrastructure expansion can meet rising bioenergy demands while minimizing costs and environmental impacts, while also highlighting trade-offs in land-use decisions and resource allocation. This model provides actionable insights for policymakers and investors while serving as both an enhancement to MACRO and NZB and a standalone tool for downscaling optimization problems.