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This paper presents a modeling framework to address the energy, economy, emissions and land use nexus when exploiting bioenergy in developing countries. The modeling framework combines a qualitative and a quantitative element. The qualitative element integrates two components: (1) technology roadmapping to identify long-term technology targets through expert judgment and (2) scenario analysis to investigate different future storylines. The quantitative element comprises four integrated tools, namely the energy system model (ESM), the land use and trade model (LUTM), an economic model, and an external climate model. An overview of the modeling framework, scenario analysis, structure of the models, modeling techniques, mathematical formulations and assumptions is presented and discussed. The modeling framework is applied to the particular context of Colombia, as a case study of a developing country with large bioenergy potential. In this study case, the impacts that an accelerated deployment of bioenergy technologies might cause on the energy demand and supply, emissions and land use until 2030 are evaluated. Results suggest that a plan to exploit bioenergy in Colombia should prioritize the deployment of technologies for biomethane production, power generation & CHP, which can reduce more GHG emissions and more emissions per incremental hectare of land than first-generation biofuels. Moreover, while the share of bioenergy in the primary energy demand decreases in all the analyzed scenarios, it is possible to envision significant increases in the share of bioenergy in road transport energy demand, power generation and natural gas supply for scenarios implementing roadmap goals. In addition, impacts of El Niño oscillation on the dependence of hydro for power generation can be partly mitigated by exploiting the complementarity of hydro and bioenergy, which might result in a reduction of up to 5–6% in the demand for fossil fuels used in power generation in dry years. However, despite the ambitious goals proposed here, bioenergy alone cannot significantly reduce emissions by 2030 (maximum 10% reduction relative to baseline) and effective climate change mitigation requires a portfolio of additional measures.
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 novel approach to address uncertainty and improve reliability of the estimation of the biomass energy potential at a country level, particularly suitable for situations when quality and availability of data are limited. The proposed methodology improves the prediction reliability by following four steps: 1) using a simple accounting framework, 2) using a robust selection of probability density functions, 3) using a probabilistic propagation of uncertainty and 4) using sensitivity analysis to identify key variables contributing to uncertainty as well as a root cause analysis and a set of sub-models to improve estimation of key variables.
The application of the methodology to the energy scenario in Colombia shows that the improved estimation of the theoretical energy potential has an almost identical mean value compared to the preliminary estimate, but the uncertainty is significantly lower (less than 50%). Moreover, the mean value of the technical energy potential obtained through the methodology is 25% lower than the preliminary potential and the uncertainty reduces by one third.
This paper describes the process of developing a technology roadmap for deploying bioenergy technologies at a country level. A method for energy technology roadmapping adapted to the conditions of developing countries is proposed. This method combines an acknowledged roadmapping framework from prior art, a new strategy to build consensus based on the Delphi method and a strong focus on analytical modeling for supporting expert judgment. This method aims to be simple, transparent and affordable. The proposed method is applied to Colombia for creating a plan to deploy sustainable bioenergy technologies in Colombia until 2030. This plan consists of a set of long-term goals, milestones, barriers and action items identified by over 30 experts for key bioenergy technology areas (viz. bioethanol, biodiesel, renewable diesel, biomethane, biogas, waste-to-energy and power generation and combined heat and power). Finally, the relevance of the process of developing a technology roadmap for bioenergy exploitation in Colombia in other developing countries is discussed.
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).