Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production Mahmood Ebadian a , Taraneh Sowlati a,⇑ , Shahab Sokhansanj b,c , Lawrence Townley-Smith d , Mark Stumborg e a Department of Wood Science, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada V6T 1Z4 b Department of Chemical & Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver, BC, Canada V6T 1Z3 c Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA d National Agroclimate Information Services, Agriculture & Agri-Food Canada, Regina, SK, Canada S4P 4L2 e Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada S9H 3X2 highlights " Studied the agricultural biomass supply chain for cellulosic ethanol production. " Evaluated the impact of storage systems on different supply chain actors. " Developed a combined simulation/optimization model to evaluate storage systems. " Compared two satellite storage systems with roadside storage in terms of costs and emitted CO 2 . " SS would lead to a more cost-efficient supply chain compared to roadside storage. article info Article history: Received 19 April 2012 Received in revised form 24 August 2012 Accepted 27 August 2012 Available online 8 October 2012 Keywords: Agricultural biomass supply chain Cellulosic ethanol Satellite storage Simulation Optimization abstract In this paper, a combined simulation/optimization model is developed to better understand and evaluate the impact of the storage systems on the costs incurred by each actor in the agricultural biomass supply chain including farmers, hauling contractors and the cellulosic ethanol plant. The optimization model prescribes the optimum number and location of farms and storages. It also determines the supply radius, the number of farms required to secure the annual supply of biomass and also the assignment of farms to storage locations. Given the specific design of the supply chain determined by the optimization model, the simulation model determines the number of required machines for each operation, their daily work- ing schedule and utilization rates, along with the capacities of storages. To evaluate the impact of the storage systems on the delivered costs, three storage systems are molded and compared: roadside stor- age (RS) system and two satellite storage (SS) systems including SS with fixed hauling distance (SF) and SS with variable hauling distance (SV). In all storage systems, it is assumed the loading equipment is ded- icated to storage locations. The obtained results from a real case study provide detailed cost figures for each storage system since the developed model analyses the supply chain on an hourly basis and consid- ers time-dependence and stochasticity of the supply chain. Comparison of the storage systems shows SV would outperform SF and RS by reducing the total delivered cost by 8% and 6%, respectively. However, RS results in 10% and 8% decline in consumed energy and produced CO 2 in logistics operations compared to SV and SF, respectively. Another finding is that the dedication of loading equipment to storage locations is an expensive option for agricultural biomass supply chain as loading operation is utilized 3%, 6% and 11% of its annual working hours in RS, SF and SV, respectively. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Despite the significant advantages of cellulosic ethanol in terms of reduction in greenhouse gas emission, economic growth in rural communities and enhancement of energy security, the production of this renewable fuel is not economically viable [1]. One of the important components for successful commercialization of cellu- losic ethanol is a secure and reliable biomass supply chain [2]. Low-density biomass from production sources has to be trans- ferred to the cellulosic ethanol plant at a low cost [3]. Low-cost delivery of biomass requires a well-established business model 0306-2619/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2012.08.049 ⇑ Corresponding author. E-mail address: taraneh.sowlati@ubc.ca (T. Sowlati). Applied Energy 102 (2013) 840–849 Contents lists available at SciVerse ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy