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Methodology for biomass energy potential estimation: Projections of future potential in Colombia
(2014)
This paper presents a novel method to estimate the future biomass energy potential in countries with domestic markets unable to influence international markets. As a study case, the biomass energy potential in Colombia is estimated for the period 2010–2030.
The prediction model is a scenario-based optimization algorithm that maximizes the yearly profit of locally producing and importing commodities in a country subject to certain constraints (domestic demand, limited area, etc.) as well as to demographic, macroeconomic and market data (e.g. domestic and international prices of commodities). The bioenergy potential associated to the production of commodities is calculated according to a methodology presented by the same authors. In order to provide a modeling framework consistent with other state-of-the-art projections, global scenarios for analysis are selected from the literature rather than formulated. Selected global scenarios highlight the influence of global biofuel use on agricultural prices, production and demand.
Results predict a theoretical bioenergy potential in Colombia 56%–69% larger in 2030 than in 2010 (1.31–1.41 EJ). A sensitivity analysis shows that while a higher global biofuel use leads to a higher local bioenergy potential, its influence is less pronounced than that of agricultural yields, demand and specific energy of biomass resources.
This paper presents a methodology to estimate the biomass energy potential and its associated uncertainty at a country level when quality and availability of data are limited. The current biomass energy potential in Colombia is assessed following the proposed methodology and results are compared to existing assessment studies.
The proposed methodology is a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential. The paper also includes a proposed approach to quantify uncertainty combining a probabilistic propagation of uncertainty, a sensitivity analysis and a set of disaggregated sub-models to estimate reliability of predictions and reduce the associated uncertainty. Results predict a theoretical energy potential of 0.744 EJ and a technical potential of 0.059 EJ in 2010, which might account for 1.2% of the annual primary energy production (4.93 EJ).