The effect of location and facility demand on the marginal cost of delivered wood chips from energy crops: A case study of the state of Tennessee

Abstract

Cost-supply curves for delivered wood chips from short rotation woody crops were calculated for 21 regularly-spaced locations spanning the state of Tennessee. These curves were used to systematically evaluate the combined effects of location and facility demand on wood chip feedstock costs in Tennessee. The cost-supply curves were developed using BRAVO, a GIS-based decision support system which calculates marginal cost of delivering wood chips to a specific location given road network maps and maps of farmgate prices and supplies of woody chips from short rotation energy crops.Marginal costs of delivered chips varied by both facility location in the state and facility demand. Marginal costs were lowest in central Tennessee unless the facility demand was greater than 2.7 million dry Mg per year (3 million dry tons per year) in which case west Tennessee was the Iowest cost region. Marginal costs rose rapidly with increasing facility demand in the mountainous eastern portion of the state. Transportation costs accounted for 18 to 29% of the delivered cost and ranged between $8 and $18/dry Mg ($7 and $16/dry ton). Reducing the expected farmer participation rate from 100% to 50% or 25% dramatically raised the marginal costs of feedstock supply in the east and central regions of the state. The analysis demonstrates the need to use geographically-specific information when projecting the potential costs and supplies of biomass feedstock.

Waste biomass-to-energy supply chain management: A critical synthesis

“…Panichelli and Gnansounou (2008) develop a methodology that employs a GIS-based approach combined with a biomass allocation algorithm for selecting suitable energy facilities location. Graham et al (1997) Zhan et al (2005) investigate the economic feasibility of locating a switchgrass-to-ethanol conversion facility in the state of Alabama. Another tool for locating conversion facilities is proposed by Papadopoulos and Katsigiannis (2002), while considering economic criteria for assessing the sustainability of the installation.…”

A Method for Particle Swarm Optimization and its Application in Location of Biomass Power Plants

“…Additionally, the greater the share of the potential fuel supply recovered, the higher the cost of procural. The effect of plant localization was also studied by Graham et al (1997Graham et al ( , 2000 In this paper, the problem to be solved consists of determining the optimal location of a forest residues-based biomass power plant. The size of the generation system depends on: 1) biomass quantity that can be collected, 2) selection of parcels where to collect the biomass.…”

Design and analysis of geographic distribution of biomass power plant and satellite storages in China. Part 1: Straight-line delivery

“…Their methodology identified power plant locations based on grid connectivity, transportation cost and energy cost. In another study, Graham et al [4] used GIS to model state-level cost of biomass (farm-gate and delivered to plant-gate) and the environmental consequences of displacing conventional crops with energy crops.…”