developing a real-time, economic-based program for the web
Transcription
developing a real-time, economic-based program for the web
Biomass Site Assessment Tool – D Developing l i a real-time, l ti economici based program for the web. Andrew J. Hartsell, Research Forester – USFS Timothy M. M Young, Young PhD – UT Donald G. Hodges, PhD – UT James H. Perdue – USFS U.S. Forest Service The University of Tennessee S h Southern Research Station h S i DOT, Sun Grant Center O S G C Forest Products Center Outline Introduction - Needs Objectives Methods M th d Results Summary/Future Research Introduction To develop a webweb-based economic decision--making model for cellulose decision resources with realreal-time data capabilities www.BioSAT.net Introduction Continued Allow for multiple feedstocks Must use the latest data • Resource Information • Travel Times • Diesel Prices Allow users the ability to refine their search criteria • Resource R Type T (softwood, ( f d clean l chips, hi mill ill residues) •Travel distance distance, time time, tonnage, tonnage or cost •Infrastructure: Railroads, water, land, people Introduction Continued BioSAT will investigate the supply of both A i l Agricultural l and dF Forest Biomass Bi Objectives j 1. 2. 3 3. 4. 5. 6 6. Develop a Microsoft SQL database of resource data (forest, mill residue, and agricultural feedstocks) Develop resource costs for database (SRTS for south, State--level Reporting for North) State Develop transportation cost model for database Develop harvesting cost model for database (FRCS, AHA) Develop webweb-based economic decisiondecision-making tool for cellulose using sites (www.BioSAT.net) Real--time (or as available) updates of key input data, Real data e.g., diesel prices, mill residue prices, FIA data, etc. complete Scope: 33 Eastern United States Scope: Resolution : ZCTA near completion initiated Methods Elementary data fusion Update as available Update as available Real-time update SQL Biofuels Database Real-time update SQL database Methods Aggregate Supply Curves Aggregate Supply = SQL Database marginal cost of resource + marginal i l costt off harvesting h ti + marginal cost of transportation, given supply limit constraints. Methods Zipcode Tabulation Areas (ZCTAs) ZCTAs provide the basic spatial structure for BioSAT. ZCTAs are based on U.S. Census Bureau census blocks. Many transportation models use zipcodes for computing travel time. The smallest spatial unit for most resource data is the county. However, counties do not provide the resolution needed for travel time analysis. Therefore, county level resource estimates are allocated to ZCTAs based on area proportionality. ti lit Example, if a ZCTA accounts for ten percent of a counties area, then ten percent of that county’s p y data is assigned g to that ZCTA. Methods Transportation / Trucking Costs Methods Trucking Cost Model Extension and Refinement of Berwak et. al. 2003 Total Cost (a, d, t) = Variable Cost (d, t) + Fixed Cost (a, d, t) where, a: annual miles d ttravell distance d: di t (miles) ( il ) t: travel time (hours) Methods Trucking Variable Costs Variable Cost (d, t) = Fuel Cost (c, d, g, j, k) + Labor Cost (i, w) + Tire Cost (c, m, n, r) + Maintenance and Repair Cost (b, c, v) d: travel d: travel distance (miles) distance (miles) t: travel time (hours) b: base repair cost per mile ( ) c: time loaded (%) g: average diesel price per gallon j: loaded truck miles per gallon k: empty truck miles per gallon i: labor time (hours) i: labor time (hours) m: miles per tire n: number of tires r: tire cost w: wage v: gross vehicle weight Methods Trucking Fixed Costs Fi d C Fixed Cost ((a, d d, t)) = (E (Equipment i C Cost + T Tax + Li License F Fee + Management and Overhead Cost + Insurance Premium) / a * d Equipment Cost is function of interest rate, purchase price, salvage rate, and useful life; useful life; Tax is function of highway use tax, purchase price, useful life, excise tax; License Fee is function of the number of tractors and trailers in fleet and a annual license fee annual license fee. a: annual miles ( ) d: travel distance (miles) t: travel time (hours) Methods Method for Finding Travel Time Currently all the zip codes with a sphere distance of no more than 80 miles are defined as potential neighboring zip codes off the h given i zip i code. d For each ppotential neighboring g g zip p code, the drivingg distance and time to the given zip code is computed by Microsoft MapPoint©. Among the potential zip codes, those with driving times of no more than five hours are defined as the neighboring zip codes of the ggiven zip p code,, i.e.,, accounts for road network geographic and economic barriers. Methods Resource & Harvesting Methods Cellulose Quantity Resource Beta 1a Beta 1b Ub W Urban Waste t BT2 BT2 Mill Residues BT2 National FIA TPO Database Other Removals BT2 BT2 Logging Residues BT2 SRTS / FRCS Thinnings BT2 BT2 Pulpwood / Sawtimber Harvest Options NA FIA / AHA / TMS-State Reports Note, the analysis presented today is for Mill & Logging Residues only Methods Cellulose Quantity – continued Logging residues (tonnage) are derived by the Subregional Timber Supply Model (SRTS) developed (Abt et al. 2000) Logging residue costs will come from the Fuel Reduction Cost Simulator (FRCS) used in the Billion Ton study, modified by Dykstra y ((2008)) Pulpwood & Sawtimber harvest options will be based on the output from the Auburn Harvest Analyzer (AHA). • Inputs will be stand tables developed from the FIA database • Timber Mart-South (south) and state reports (north) will provide the price information R Results lt Results 15 lowest cost ZCTAs for 80 mile travel distance Summary www.BioSAT.net www BioSAT net decision decision--making tool for cellulose using sites based on transportation costs and road networks for 33 eastern states Forest resource (billion ton study, pulpwood, and sawtimber) and mill residue database for 33 eastern states Near Completion: Forest harvesting costs by zip code (FRCS & AHA) Forest resource costs (SRTS for south) Beta Beta--version of Future Research Resource costs for northern/eastern states in development p Reallocation of resource data based on land cover Agricultural resource database in development Resource costs for f agricultural l l resources in development Agricultural harvesting cost model in development Railroad networks with intraintra-modal transfer points Acknowledgements g • USDA Forest Service Southern Research Station US DOT Southeastern Sun Grant Center University of Tennessee Office of Bioenergy Programs U i University it off Tennessee T Agricultural A i lt l Experiment E p i t Station St ti • • • • • Xu (Nancy) Liu, GRA; Yingjin Wang, former GRA Sam Jackson, Research Assistant Professor, UT Kerri Norris, Research Associate, UT Christy Pritchard, Research Associate, UT Sachiko Hurst Hurst, Programmer, Programmer UT • University of Tennessee College of Business North Carolina State University Any Questions?