Sierk de Jong, Ric Hoefnagels, Elisabeth Wetterlund, Karin - - PowerPoint PPT Presentation

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Jan 12, 2024 23 likes •153 views

Copernicus Institute of Sustainable Development Economies of scale in bioenergy theory vs practice Sierk de Jong, Ric Hoefnagels, Elisabeth Wetterlund, Karin Pettersson & Martin Junginger Copernicus Institute of Sustainable Development

SLIDE 1

Copernicus Institute of Sustainable Development Copernicus Institute of Sustainable Development

Economies of scale in bioenergy – theory vs practice Sierk de Jong, Ric Hoefnagels, Elisabeth Wetterlund, Karin Pettersson & Martin Junginger

SLIDE 2

Copernicus Institute of Sustainable Development

In the oil industry bigger is usually cheaper, in biofuel it is more complex

Production scale Production costs (€/GJ) Production scale Production costs (€/GJ)

?

Oil industry Biofuel

SLIDE 3

Copernicus Institute of Sustainable Development

A stylized example of the biofuel supply chain

∝ 𝛾𝑌

1 2 , 𝑥ℎ𝑓𝑠𝑓 𝛾 ∝

𝑢𝑑𝑤 𝜍

1 2

𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 𝑥𝑗𝑢ℎ 𝑌

Theoretical scale curve*

Production cost (€/GJ biofuel) Production scale X Transport CAPEX OPEX Feedstock ∝ 𝛽𝑌𝑡𝑔−1 , 𝑥ℎ𝑓𝑠𝑓 𝛽 ∝ 𝐽 𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 𝑥𝑗𝑢ℎ 𝑌

Our biofuel supply chain

*Where X is scale, tcv the variable transport cost, sf the scaling factor and I the investment

SLIDE 4

Copernicus Institute of Sustainable Development

𝑌 = 𝛾 2𝛽 1 − 𝑡𝑔

1 𝑡𝑔−1.5

The optimal capacity 𝒀 depends on technological scalability and capital intensity, feedstock density and transport cost

Decreasing scaling factor Increasing capital intensity Increasing transport cost Decreasing feedstock density

𝒀 𝒀 𝒀

Example Pyrolysis

tcv = 0.1 €/tkm I = 350 M€ @ 400MW Sf = 0.7 ρ = 30 t/km2/yr

𝐘 ~ 7 Mt/yr

SLIDE 5

Copernicus Institute of Sustainable Development

However, in practice there are more parameters which affect the theoretical scale curve

Scale X Production cost (€/GJ biofuel)

Maximum capacity Theoretical scale curve Maximum capacity In practice, material limitations, shipping limits, site size and frame size may curb the size of (parts of) a conversion plant

SLIDE 6

Copernicus Institute of Sustainable Development

Distributed supply chain configurations can aid to limit the impact of growing transportation cost

Centralized supply chain Distributed supply chain

Lower CAPEX, higher transportation cost Higher CAPEX, lower transportation cost

Legend Feedstock Pre-conversion unit Conversion unit Storage terminal

SLIDE 7

Copernicus Institute of Sustainable Development

However, in practice there are more parameters which affect the theoretical scale curve

Scale X Production cost (€/GJ biofuel)

Maximum capacity Theoretical scale curve Additional factors affecting the scaling curve in practice, e.g.

Maximum capacity
Supply chain configurations
Inhomogeneous feedstock

density

Inhomogeneous feedstock

price

Competing demand
Transport infrastructure
Transport modes
Integration with host

industries Distributed supply chain configuration Maximum capacity In practice, material limitations, shipping limits, site size and frame size may curb the size of (parts of) a conversion plant

SLIDE 8

Copernicus Institute of Sustainable Development

Linear

ptimization

model

Optimizing system** cost for a given biofuel demand Techno-economic data Conversion sites

Forest terminals
Pulpmills
Sawmills
District heating
Refineries
LNG terminals
Natural gas pipeline

connection

**System = Biofuel & competing industry

Spatially explicit for Sweden

We used an optimization model to develop a scale curve for biofuel production in Sweden

Transport modes

Truck
Train
Short sea

HTL* Hydro- processing Blending terminal

*Hydrothermal liquefaction

Feedstock

CAPEX (dependent on scale & site)
OPEX (dependent on site)
Constraint on maximum capacity

Feedstock supply & prices

Forestry residues
Byproducts from

saw- and pulpmills

Stumps
Sawlogs
Pulpwood

Competing feedstock demand District heating Pulp and paper Sawmills

SLIDE 9

Copernicus Institute of Sustainable Development

The model can choose between centralized and distributed supply chain configurations at different locations

Centralized supply chain Distributed supply chain

Legend Feedstock HTL Hydroprocessing Storage terminal Potential locations: Refineries, natural gas grid connection, LNG terminal Potential upgrading locations: Refineries, natural gas grid connection, LNG terminal Potential HTL locations: Pulp mill, sawmill, district heating, forest terminals

SLIDE 10

Copernicus Institute of Sustainable Development

At a plant level economies of scale and the maximum capacity determine the shape of the scaling curve

20 15 10 5 15 18 24 22 17 20 25 23 16 21 19 Plant scale (PJ) Production cost (€/GJ biofuel)

Centralized: low CAPEX, high upstream transport cost Distributed: high CAPEX, low upstream transport cost Observations

1. Jigsaw curve due to maximum capacity
2. Downward trend beyond maximum plant

capacity

3. Distributed over centralized supply chains

at small capacities

Preliminary results, please do not cite

SLIDE 11

Copernicus Institute of Sustainable Development

5 10 15 20 25 200 50 150 100 Total biofuel production (PJ) Production cost (€/GJ biofuel) Total Conversion and upgrading Upstream transport Downstream transport Feedstock Intermediate transport

On a system level the cost curve has an upward tail which is caused by increasing feedstock prices, not by transport cost

Observations

1. Convex total cost curve
2. Interplay between conversion cost

and feedstock cost; relatively constant upstream transport cost

3. Preference for distributed

configurations at higher scales One upgrading plant More upgrading plants

Preliminary results, please do not cite

SLIDE 12

Copernicus Institute of Sustainable Development

Key determinants for scaling Economies of scale and maximum achievable capacity are the most important determinants in the biofuel scaling curve, not transportation costs (unlike theory) Distributed vs. centralized Distributed supply chain configurations are favored over centralized

nes at small scale due to integration benefits and preferential siting

(unlike theory) System’s perspective From a system’s perspective distributed supply chain configurations are favored, as there are limited locations at which centralized production makes sense (combination of high feedstock density and required utilities)

Preliminary conclusions

SLIDE 13

Copernicus Institute of Sustainable Development

Economies of scale in bioenergy – theory vs practice Sierk de Jong, Ric Hoefnagels, Elisabeth Wetterlund, Karin Pettersson & Martin Junginger

In the oil industry bigger is usually cheaper, in biofuel it is more complex

Production scale Production costs (€/GJ) Production scale Production costs (€/GJ)

?

Oil industry Biofuel

A stylized example of the biofuel supply chain

∝ 𝛾𝑌

𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 𝑥𝑗𝑢ℎ 𝑌

Theoretical scale curve*

Production cost (€/GJ biofuel) Production scale X Transport CAPEX OPEX Feedstock ∝ 𝛽𝑌𝑡𝑔−1 , 𝑥ℎ𝑓𝑠𝑓 𝛽 ∝ 𝐽 𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 𝑥𝑗𝑢ℎ 𝑌

Our biofuel supply chain

𝑌 = 𝛾 2𝛽 1 − 𝑡𝑔

The optimal capacity 𝒀 depends on technological scalability and capital intensity, feedstock density and transport cost

𝒀 𝒀 𝒀

Example Pyrolysis

𝐘 ~ 7 Mt/yr

However, in practice there are more parameters which affect the theoretical scale curve

Scale X Production cost (€/GJ biofuel)

Distributed supply chain configurations can aid to limit the impact of growing transportation cost

Centralized supply chain Distributed supply chain

Lower CAPEX, higher transportation cost Higher CAPEX, lower transportation cost

However, in practice there are more parameters which affect the theoretical scale curve

Scale X Production cost (€/GJ biofuel)

Linear

model

Spatially explicit for Sweden

We used an optimization model to develop a scale curve for biofuel production in Sweden

HTL* Hydro- processing Blending terminal

Feedstock

saw- and pulpmills

The model can choose between centralized and distributed supply chain configurations at different locations

Centralized supply chain Distributed supply chain

At a plant level economies of scale and the maximum capacity determine the shape of the scaling curve

20 15 10 5 15 18 24 22 17 20 25 23 16 21 19 Plant scale (PJ) Production cost (€/GJ biofuel)

Centralized: low CAPEX, high upstream transport cost Distributed: high CAPEX, low upstream transport cost Observations

5 10 15 20 25 200 50 150 100 Total biofuel production (PJ) Production cost (€/GJ biofuel) Total Conversion and upgrading Upstream transport Downstream transport Feedstock Intermediate transport

On a system level the cost curve has an upward tail which is caused by increasing feedstock prices, not by transport cost

Observations

Key determinants for scaling Economies of scale and maximum achievable capacity are the most important determinants in the biofuel scaling curve, not transportation costs (unlike theory) Distributed vs. centralized Distributed supply chain configurations are favored over centralized

(unlike theory) System’s perspective From a system’s perspective distributed supply chain configurations are favored, as there are limited locations at which centralized production makes sense (combination of high feedstock density and required utilities)

Preliminary conclusions

Thank you for your attention!

Sierk de Jong Utrecht University s.a.dejong@uu.nl

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