Optimizing a pellet supply system: Market-specific pellet production with biomass quality considerations

Abstract

Renewable energy policies have driven the wood pellet market over the last decades worldwide. Among other factors, the returns from this business depends largely on how well the producers manage the uncertainty associated with biomass yield and quality. To serve this purpose, this study develops a two-stage stochastic programming model that optimizes different critical decisions (e.g., harvesting, storage, transportation, quality inspection, and production decisions) of a biomass-to-pellet supply system under biomass yield and quality uncertainty. The goal is to economically produce pellets while accounting for the different pellet standards set forward by the U.S. and European markets. Due to the difficulty in solving the optimization model in a real-world scenario, we develop a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging algorithm. We propose two parallelization schemes to efficiently speed up the convergence of the overall algorithm. We use Mississippi as a testing ground to visualize and validate the performance of the algorithms. Experimental results indicate that the biomass-to-pellet supply system is sensitive to the biomass quality parameters (e.g., ash and moisture contents). Results further reveal that the pellet production can drop up to 13.6% to 48.3%, respectively, if high ash or moisture content is available in the biomass.