Technological learning in bioenergy systems

The main goal of this article is to determine whether cost reductions in different bioenergy systems can be quantified using the experience curve approach, and how specific issues (arising from the complexity of biomass energy systems) can be addressed. This is pursued by case studies on biofuelled combined heat and power (CHP) plants in Sweden, global development of fluidized bed boilers and Danish biogas plants. As secondary goal, the aim is to identify learning mechanisms behind technology development and cost reduction for the biomass energy systems investigated. The case studies reveal large difficulties to devise empirical experience curves for investment costs of biomass-fuelled power plants. To some extent, this is due to lack of (detailed) data. The main reason, however, are varying plant costs due to differences in scale, fuel type, plant layout, region etc. For fluidized bed boiler plants built on a global level, progress ratios (PRs) for the price of entire plants lies approximately between 90–93% (which is typical for large plant-like technologies). The costs for the boiler section alone was found to decline much faster. The experience curve approach delivers better results, when the production costs of the final energy carrier are analyzed. Electricity from biofuelled CHP-plants yields PRs of 91–92%, i.e. an 8–9% reduction of electricity production costs with each cumulative doubling of electricity production. The experience curve for biogas production displays a PR of 85% from 1984 to the beginning of 1990, and then levels to approximately 100% until 2002. For technologies developed on a local level (e.g. biogas plants), learning-by-using and learning-by-interacting are important learning mechanism, while for CHP plants utilizing fluidized bed boilers, upscaling is probably one of the main mechanisms behind cost reductions.     

Designing a cost-effective CO2 storage infrastructure using a GIS based linear optimization energy model

Large-scale deployment of carbon capture and storage needs a dedicated infrastructure. Planning and designing of this infrastructure require incorporation of both temporal and spatial aspects. In this study, a toolbox has been developed that integrates ArcGIS, a geographical information system with spatial and routing functions, and MARKAL, an energy bottom-up model based on linear optimization. Application of this toolbox led to blueprints of a CO₂ infrastructure in the Netherlands. The results show that in a scenario with 20% and 50% CO₂ emissions reduction targets compared to their 1990 level in respectively 2020 and 2050, an infrastructure of around 600 km of CO₂ trunklines may need to be built before 2020. Investment costs for the pipeline construction and the storage site development amount to around 720 m€ and 340 m€, respectively. The results also show the implication of policy choices such as allowing or prohibiting CO₂ storage onshore on CO₂ Capture and Storage (CCS) and infrastructure development. This paper illustrates how the ArcGIS/MARKAL-based toolbox can provide insights into a CCS infrastructure development, and support policy makers by giving concrete blueprints over time with respect to scale, pipeline trajectories, and deployment of individual storage sites.     

Exploration of the land potential for the production of biomass for energy in the Netherlands

The energy potential for energy crops and biomass residues in the Netherlands is assessed. The analysis explores the possible use of land for biomass production in the future. Various government memorandums and analyses of the expected future land use in various sectors have served as the basis for the assessment of the supply of and the demand for land in the future. In this study the potential supply of agricultural land is based on expected productivity increments in agriculture and assumptions with respect to the future demand for agricultural products. Various future claims for infrastructure, forestry, urban areas and nature are subtracted from the expected supply. The net projected supply of land ranges from zero to 52 000 ha in 2000 to 110 000-250 000 ha in 2015. The supply of agricultural land depends however on a number of supra-national factors, such as the European agricultural policy, world market developments and the agricultural production in the countries in Eastern Europe. Uncertainties remain, therefore, and the projected supply of agricultural land should be considered as a possible scenario based on current trends. If the calculated land potential is used for energy crops like miscanthus and short rotation coppice, this land could contribute 0-10 PJ in 2000 and 27-59 PJ in 2015. Secondary biomass yields, such as those from forestry, agricultural residues, wood from prunings, etc., could contribute a further 34 PJ in 2000, decreasing to approximately 28 PJ in 2015. Taken together these potentials could satisfy 1-1.5% of the energy requirements of the Netherlands in 2000 and 1.5-2.5% in 2015, provided that energy farming is an economically feasible activity for farmers.     

Greenhouse gas footprints of different biofuel production systems

The aim of this study is to show the impact of different assumptions and methodological choices on the life-cycle greenhouse gas (GHG) performance of biofuels by providing the results for different key parameters on a consistent basis. These include co-products allocation or system expansion, N₂O emissions from crop cultivation, conversion systems and co-product applications and direct land-use change emissions. The results show that the GHG performance of biofuels varies depending on the method applied and the system boundaries selected. Key factors include selected allocation procedures and the location of production and related yields, reference land and soil N₂O emissions.