VIEWS: 5 PAGES: 29 POSTED ON: 8/28/2009
Efficiënte bio-energiesystemen voor de toekomst: techno-economische aspecten Onderzoeksprojecten: – BioPUSH: Integrated Strategies for Identifying Optimal Bio-Energy Production and Utilisation Systems – AIRE: Accelerated implementation of a renewable electricity supply in the Netherlands Current biomass use is rather low • Biomass can play important role in GHG mitigation and renewable energy supply • Currently, rather low share of biomass for material and energy applications, especially from dedicated energy crops • In Europe, main reasons are: – high production costs – relative low availability of agricultural land more competitive alternatives for the introduction of biomass are needed – high specific CO2 emission reduction – high profitability Identification and implementation of promising biomass system • How to produce/collect biomass resources efficiently? • What type of uses for energy and/or material are favorable given market conditions and GHG emission reduction goals? • How does the efficiency and production costs of biomass systems improve with learning? BioPUSH: Multi-functional biomass systems, PhD-thesis, Veronika Dornburg, Copernicus Institute, University Utrecht Central question of the thesis What is the potential of multi-functional biomass systems to improve the costs and the land use efficiency of saving non-renewable energy consumption and reducing GHG emissions in quantitative terms? Efficiency of multi-functional biomass systems • Resources are limited: – Total global potential of biomass about 50-200 EJ/yr in 2050 (primary energy consumption in 2001 about 420 EJ/yr). – Dedicated crops are necessary • Comparison of different systems with efficiency criteria: – – – – Savings of non-renewable energy consumption GHG emission reductions Agricultural land use Costs of biomass systems potential benefits of multi-functional biomass systems still need to be proven in quantitative analyses. Methodological issues • Methods for GHG emission reductions in development adaptation or development of methodologies needed – allocation to account for different products and land uses – accounting for time dimension in long-life cascading applications – integration of market price changes due to the largescale introduction of biomass systems Land use – production of biomass Wood (short/long term rotation) Perennial herbaceous crops (e.g. miscanthus) Other crops (oilseed, sugar, starch) Both uses from one crop: multi-product use Material use Construction Food/fodder Chemicals Pulp and paper Other Energy use Waste-to-energy + Recycling: cascading Electricity Heat Fuels Multi-functional biomass systems Multifunctional systems studied 1. – Bioenergy production and multi-product crops Case study of hemp, wheat, poplar in NL and PL 2. – Biomass cascading systems Methodological aspects and case study of SRF poplar 3. – Land requirements of bio-plastics (and bio-energy) A review and system analysis of LCAs 4. – PLA bio-refinery system Combining bottom-up analysis with price elasticity 5. – GHG emission mitigation supply curves Analysis of large-scale biomass use on a country level Results - Annual CO2 emission reduction per ha of biomass production 35 30 25 Mg CO2 per ha 20 15 10 5 0 -5 -10 EL ME LU-EL ET-EL VI-EL LU-LU PA-MDF PA-LU PA-LU PA-EL MDF-EL PUL-EL -ET-EL -ET-EL -MDF-EL -LU-EL EL: electricity ME: methanol LU: particle lumber MDF: MDF board PA: Pallet PUL: Pulp ET: Ethylene CO2 reduced per ha CO2 reduced per ha evaluated with present value VI: Viscose Break-down of chain analysis GHG emissions and GHG emission reductions [Mg CO2eq/Mg biomass input] 6 Costs and revenues [kEuro/Mg biomass input] 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 0 1 2 3 4 5 6 7 8 9 10 11 12 4 2 0 -2 -4 0 1 2 3 4 5 6 7 8 9 10 11 12 crop production transport bio-energy PLA production subst.by-prod substitution petrochem. polymer recycling waste treatment Amount of biomass (PJHHV) 500 1000 1500 2000 200 Marginal GHG emission mitigation costs ( Euro/Mg CO 2eq) 100 Scenario V3 Scenario V4 Scenario V1 Scenario V2 0 -100 total supply V2 0 20x106 40x106 60x106 80x106 total supply V3 total total supply supply V4 V1 100x106 120x106 'Integral' GHG emission mitigation cost supply curves for the different scenarios Amount of biomass (Mgdry biomass) Overall conclusions: quantification In comparison to bio-energy systems: • Multi-product systems – decrease biomass fuel costs by to more than 50 €/GJLHV (compared to 215 €/GJLHV biomass) – but lower GHG emission reductions with 3-10 Mg CO2eq /ha*yr • Cascading – alters GHG emissions avoided by –13 to 23 Mg CO2 /ha*yr – modifies GHG mitigation costs by –300 to 2000 €/Mg CO2 – (use of short rotation wood for electricity results in avoided emissions of 5 Mg CO2 p/ha*yr and costs of 100 €/Mg CO2) • Use of agricultural residues for energy production – increases benefits of bio-based polymers by up to 15 Mg CO2eq/ha*yr • Multi-functional biomass use in bio-refinery system – leads to additional benefits of 4-12 Mg CO2eq/ha*yr and 0-200 €/Mgbiom Methodological lessons • Inclusion of agricultural land use in comparison of biomass systems can provide valuable insights in their ranking • Necessary to account for land use in the reference system => standard methodology needed • Time dimensions of carbon storage in materials important (see PV method) => standard methodology needed • Scale of biomass production and use influences costs of the system as well as GHG emission balances Summary and recommendations • To use biomass efficiently multi-functional biomass systems can be an attractive option – (depending on design, reference systems and land, material and energy markets) • For large-scale biomass systems, the interactions of biomass use with land, material and energy markets need to be better understood. • Research on optimal biomass systems for GHG emission mitigation should combine bottom-up information with knowledge on market mechanisms from top-down analyses ? Case study within AIRE: Technological Learning and Cost Reductions of Biomass CHP Combustion Plants - The Case of Sweden Martin Junginger (Utrecht University) Background • Cost of electricity from biomass combustion technologies can in many cases not (yet) compete with electricity from fossil fuel power plants • For a sustainable energy supply, further cost reductions are necessary • Past cost reductions can provide insights in further cost reduction potential Aim: • To investigate and quantify cost reductions in biomass energy systems • To determine underlying factors • To test the applicability of the experience curve concept, and to investigate necessary methodological adaptations If so, the experience curve may provide a valuable tool for assessing future production costs The experience curve methodology Emperically observed many times: With every doubling of cumulative production Production costs tend to fall with a fixed percentage Progress Ratio = 80% 20% 20% 20% 20% 20% Source: Harmon, IIASA, 2000 The biomass CHP learning system 1. Experience curve on investment cost Investment in biofuelled CHP technology Output: Capacity (kW e) Input (€) Main components: Fuel feeding system Boiler Generator Turbine Heat exchanger Flue gas cleaning Flue gas recovery Operation and maintenance of the Input (€) CHP plant Output: Full-load hours (h) Input (€) Output: Production of Electricity (kWhe) electricity from biofuelled CHP plants Experience gained with plant operation Input (€) Fuel preparation Supply of biofuel Output: Fuel (kWhth) 2. Experience curve on fuel supply 3. Experience curve on electricity production cost a) Biomass CHP plant investment cost development Specific investment costs (Euro(2000)/kWe) 10000 1000 PR = 91% R2 = 0.18 10 100 1000 Cumulative installed capacity (MWe) Cost reduction trend visible, but not statistically significant b) Reduction of Primary Forest Fuel cost One important cost reduction factor: Leaning-by-doing PFF is piled already during roundwood harvest, facilitating forwarding b) Results: PFF experience curve Prices of unrefined forest fuels (€(02)/MWh) 50 1975 40 1980 30 1985 20 R2 = 0.938 PR = 85.6% Variation PR 84.5 - 85.9% under a high / low production scenario) 10 0.1 1 10 1990 2003 1995 100 200 Cumulative production of PFF in Sweden 1975-2003 (TWh) c) Experience curve on biomass CHP electricity generation 18 CHP plants in Sweden 1991-2002. The standard deviation of the calculated averages is also presented Conclusions and recommendations: • Experience curve methodology was shown to be applicable for forest fuel supply chains, and to a somewhat lesser extent for biomass electricity production • PFF experience curve may also be used for countries with less production experience (e.g. eastern Europe) to estimate future production cost developments Assumptions on technological learning can strongly influence expected renewable energy mix in Europe 160 140 120 100 80 60 40 20 0 2000 • Offshore wind farms and biomass gasification plants are „rivals‟ • Biomass gasification break-through strongly depends on number of pilot plants built Annual electricity production (TWh) 2005 Biomass CHP OTLS 2010 2015 2020 Biomass combustion OTLS Wind offshore OTLS Biomass combustion PTLS Wind offshore PTLS Biomass gasification OTLS Biomass CHP PTLS Biomass gasification PTLS General recommendations for biomass technologies: • Significant cost reductions (up to a factor of three) are possible through learning-by-doing and acquiring experience, but it takes time and nichemarket protection (“learning investments”) necessary, e.g. financing of pilot plants, feed-in tariff support etc. • The same holds for new biomass technologies, e.g. advanced pretreatment options such as torrefaction, or biomass gasification for electricity production and production of 2nd generation transportation fuels • Experience curves can support estimates for policy makers on the required investments and potential for further cost reductions ?
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