Economic Analysis of Biomass Supply Chains: A Case Study of Four Competing Bioenergy Power Plants in Northwestern Ontario
Author(s) -
Md. Bedarul Alam,
Reino Pulkki,
Chander Shahi,
Thakur Prasad Upadhyay
Publication year - 2012
Publication title -
isrn renewable energy
Language(s) - English
Resource type - Journals
eISSN - 2090-746X
pISSN - 2090-7451
DOI - 10.5402/2012/107397
Subject(s) - gross margin , bioenergy , biomass (ecology) , profit maximization , renewable energy , procurement , environmental science , supply chain , agricultural engineering , biofuel , power station , profit (economics) , profit margin , environmental economics , production (economics) , natural resource economics , economics , engineering , business , waste management , microeconomics , ecology , electrical engineering , management , marketing , biology , finance
Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on green energy sources. This paper develops and applies two optimization models to analyze the impacts of biomass competition on cost structures and gross margins for four competing biomass-based power plants in northwestern Ontario. Model scenarios are run to study the impacts of changes in parameters relevant to biomass type and processing technology, and prices of inputs and outputs on procurement costs. Cost minimization model shows that per unit procurement costs are directly proportional to the size of the power plants in all scenarios. Profit maximization model, on the other hand, shows that FMUs that are closer to the power plants make higher gross margins. However, the margins significantly increase for FMUs that are close to the power plants potentially offering higher prices. The variations in costs and gross margin structures under various model scenarios are explained by location of depletion cells relative to power plants, availability of each type of biomass in depletion cells, biomass demands, and differential processing costs for two types of biomass. These results can aid decision makers to make improved decisions related to biomass supply chains for bioenergy production.
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