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					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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posted:8/28/2009
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Lingjuan Ma Lingjuan Ma MS
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