System level analysis of biomass feedstock production for bioenergy sector

The success of biomass based energy sector depends critically on an efficient, costeffective and sustainable biomass feedstock production system supporting the biorefinery. Spatially distributed collection of the low energy density feedstock demands a highly efficient system to ensure cost competitiveness. Further challenges arise as seasonal availability of energy crops must support year-round demand to operate refinery on a continuous basis. Consequently, an integrated system level analysis is necessary to coordinate various feedstock production related tasks. Such an analysis should incorporate not only planning level (long term) but also management and operational level (short term) aspects. This article presents the research conducted in developing a feedstock production optimization model as a step in developing such a framework. The breadth level optimization model incorporates different tasks such as harvesting, packing, storage, biomass handling and transportation that are essential for feedstock production. The objective is to determine the optimal configuration of the feedstock production system on a regional basis. The decision variables include the design/planning level decisions such as equipment selection and transportation mode selection, and also the management level decisions such as daily farm management and transportation fleet scheduling. This leads to the formulation of a mixed integer linear programming (MILP) problem. An economic objective function is formulated and established techniques from mathematical programming are used to solve the computationally challenging problem. Other performance indicators such as energy consumption and greenhouse gas emissions are also tracked for comparison. The model has been applied for the case of switchgrass production as the energy crop in southern Illinois. The results show that the optimal machine selection and storage decisions depend on the farm-size, and also highlight the importance of developing an optimization model.