Optimization of decentralized energy systems using … · Optimization of decentralized energy systems using biomass resources for rural electrification in developing countries Diego

Optimization of decentralized energy systems using biomass resources for rural electrification

in developing countries

Diego Silva, Toshihiko Nakata

Tohoku University, Graduate School of EngineeringDepartment of Management Science and Technology

Sendai, Japan

June 23, 2009

IAEE 32nd International Conference, San Francisco

Contents

• Introduction• Methodology• Results and discussion• Conclusion and future tasks

2Diego Silva and Toshihiko Nakata - Tohoku University



Energy access and the MDGs


1.6 billion without electricity in 2005 (IEA)

2.4 billion rely on traditional biomass in 2005 (IEA)

1.6 million deaths due to indoor air pollution in 2004 (WHO)

Electrification schemes

5Diego Silva and Toshihiko Nakata ‐

Tohoku University

Grid electricity

Biomass

Other renewables

Extension of gridRural areas

Urban area

Areas connected to the grid

Remote areas outside the grid

Fossil fuels

Foreign resources

Local resources

Centralized

Decentralized

Previous studies ‐

Rural electrification

• Evaluation of technologies or a set of technologies.

• Decentralized electrification with renewables or combined

with diesel (hybrid configurations).

• Allocation of agricultural resources for energy.

• Optimization methodology is most common approach.


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Research goal

• Evaluate a decentralized energy system for rural

electrification in developing countries using local biomass

resources.

• Incorporate into the optimization differences in energy

consumption and income levels between urban and rural

areas.


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Target area• South America, Colombia, Meta department

• Proximity to capital city

• Agricultural activities – Rice, sugarcane, oil palm

• Areas connected to the

electricity grid– National Interconnected System (NIS)

• Areas not connected to the

electricity grid– Non Interconnected zones (NIZ)


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Target area• Energy supply expensive in remote areas (NIZ)

– Electricity price is same as paid by middle income houses in the

interconnected area

– Diesel fuel 1.8 times more expensive


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NIS

Total population 724,929

Population with electricity 86%

Population without electricity 14%

NIZ

Total population 18,668

Population with electricity 86%

Population without electricity 14%

Proposed energy system


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-Energy resources Energy demandEnergy transmission

and distributionEnergy conversion

technologies

Foreign resources Residential sector

Local resources

Electric grid

Diesel fuel

Rice husk

Bagasse

Sugarcanewaste

Dieselgeneration

Directcombustion

Gasification

Electricitysupply NIS

Electricitysupply NIZ

Pyrolysis

NIS-Urban

NIS-Rural

NIS-Populatedcenter

NIZ

Forest biomass

NIS: National interconnected systemNIZ: Non interconnected zones

Results

Linear programming• Minimumoverrun (net)costs

Sensitivity analysis • Biomass share

Optimization model

Flow of analysis


Parameters • Costs of technologies and resources

•Conversion efficiency

Constraints• Energy resources stock• Electricity demand• Resource location• Utilization of agricultural wastes

Additional input• Urban-rural differences (prices, resources)

• Socio-economic strata (prices)

Energy system structure• Technologies• Resource

allocation

Energy system performance • Costs• CO2 emissions• Regional

differences

Input data

Optimization• Linear programming (LP) formulation


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( _ cos ) ( _ ) klk l

B Tota l ts To ta l revenue b= − = ∑ ∑

ijk j ijkj i

k l kl kl klkl

l

c qd b p d

d

η⎡ ⎤⎢ ⎥ × − =⎢ ⎥⎢ ⎥⎣ ⎦

∑ ∑∑

ijk ij k

q r≤∑ ∑

Minimize overrun costs : Min B

bkl

: overrun costs (net costs)B : total overrun costscijk

: unit electricity generation costdkl

: electricity demandpkl

: unit price of electricityqijk

: primary energy resource ηj

: electricity conversion efficiencyi : energy resourcej

: energy conversion technologyk : locationl : energy demand sector and socio‐economic stratum

Case setting


• Baseline

• Cases analyzed– Biomass‐Remote: biomass based electricity for areas outside the grid

– Biomass‐Rural: biomass based electricity for all rural areas

– Biomass‐All: biomass based electricity for all areas

Input data


Availability and cost of resourcesResource Stock Cost

ton/yr US$/kgDiesel fuel - 0.290*

Rice husk 43,840 0.080

Bagasse 14,577 0.010

Sugarcane wastes 23,122 0.014

Natural forest wastes 198,158 0.030

Planted forest wastes 138,772 0.030*Diesel fuel cost for NIZ is US$0.530/kgCalculated based on data from UPME (2003).

Input data


Features of conversion technologies considered

Conversion technologyEfficiency Capital cost O&M costs

% US$/kW US¢/kWh

Diesel 30 300 1.70

Direct Combustion 17.5 2,300 0.05

Gasification 23.9 4,200 0.07

Pyrolysis 24.7 3,600 0.21Data from Solantausta and Huotari (1999) and UPME (2000)Scale of plants = 2 MWRate of return = 10%, lifetime = 20 years.Baseline for emissions reduction : NIS served with the grid and NIZ with diesel generationCO2 emissions factor for grid electricity and diesel generation are 0.439 kg/kWh and

0.882 kg/kWh respectively.

Input data


LocationElectricity demand

GWh/yr

NIS-Urban 192,011

NIS-Rural 28,351

NIS-Populated center 5,984

NIZ 3,517

Demand-Total 228,819

Calculated from data in UPME (2000) and SSP-SUI database.

Electricity price by socio-economic stratum US¢/kWh

1 2 3 4 5 6 Other

3.5 4.2 6.0 7.0 8.4 8.4 8.4

6.0 n.a. n.a. n.a. n.a. n.a. 14.4

Ramirez Gomez, S. (2007).



0

200

400

600

800

1,000

Grid

_ext

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Die

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Gas

ifica

tion

Pyr

olys

is

Grid

_ext

ensi

on

Die

sel_

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Dir_

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Gas

ifica

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Pyr

olys

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Grid

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Grid

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Pyr

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Baseline Biomass-Remote Biomass-Rural Biomass-All

Res

ourc

e al

loca

tion

(103

GJ/

yr)

Planted_forest_wasteNatural_forest_wasteSugarcane_wasteBagasseRice_huskDiesel_fuel_NIZDiesel_fuelElec_Grid

Energy system structure


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System performance


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Baseline Biomass- Remote Biomass-Rural Biomass-

All

Overrun costs 103 US$ 3,889 6,539 6,588 11,463

Unit electricity cost UScent/kWh 7.4 8.4 8.5 10.4

Ratio of overrun costs to total costs % 21 31 32 44

Total CO2 emissions 103 t-CO2 117,452 85,211 84,331 0

Unit emissions reduction kg-CO2 /kWh 0.00 0.13 0.13 0.48

0.00

0.05

0.10

0.15

0.20


Uni

t ele

ctric

ity c

ost (

US

$/kW

h)

Urban/rural differences


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NIS-UrbanNIS-RuralNIS-Pop_CenterNIS-Non_elecNIZ-RuralNIZ-Non_elecAverage

0

2,000

4,000

6,000

8,000

10,000

12,000


Ove

rrun

cost

s (1

0 3

US

$/yr

)

NIZ-Non_elec

NIZ-Rural

NIS-Non_elec

NIS-Pop_Center

NIS-Rural

NIS-Urban

Urban/rural differences


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-25

0

25

50

75

100


Rat

io o

f ove

rrun

cost

s to

tota

l cos

ts (%

)

NIS-UrbanNIS-RuralNIS-Pop_CenterNIS-Non_elecNIZ-RuralNIZ-Non_elecAverage

Sensitivity analysis


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0

25,000

50,000

75,000

100,000

125,000

0

10,000

20,000

30,000

0 20 40 60 80 100

CO

2em

issi

ons

(103

t-CO

2/ y

r)

Cos

t (10

3U

S$/

yr)

Share of biomass in electricity generation (%)

Total cost

Overrun cost

CO2 emissions

0

5,000

10,000

15,000

20,000

0 20 40 60 80 100

Cos

t (10

3U

S$/

yr)

Share of biomass in electricity generation (%)

Local resources

Foreign resources

Discussion

• Biomass potential for electricity generation (direct

combustion of biomass).

• Opportunities and barriers for decentralized electrification

with local biomass resources.– Emissions reduction.

– Reduced overrun costs in remote areas.

– Impact on rural (local) development, local business, income and

employment (biomass energy supply chain).

– Balancing costs of foreign resources with use of local resources.– Stable supply of electricity.– Costs, investments, prices (transportation), financial mechanisms

(subsidies).


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Conclusion

• Possibility of decentralized electrification with local biomass

resources (direct combustion of agricultural and forest wastes).

• Optimal system for decentralized electrification results in – Electricity cost 8.4‐10.4 UScent/kWh, higher than baseline

(7.4 UScent/kWh).

– Overrun costs of biomass based system 1.5 to 4 times larger.

– Emissions reduction 120x103

t‐CO2

/yr compared to baseline.

• Impact on rural development. – Smaller differences in the proportion of overrun costs.

– Costs of local and foreign resources.


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Further analysis

• Improvements with respect to factors overlooked by the

model (transportation, stable supply).– Enlarged scope of the energy system (heat demand, other sectors).

– Plant scale considerations.– Transport costs, resource geographical distribution (GIS).

• Alternative model formulation (multi‐objective programming,

dynamic programming).


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THANK YOU FOR YOUR ATTENTION!

Diego Silva and Toshihiko Nakata ‐

Tohoku University 28

Documents

Optimization of decentralized energy systems using … · Optimization of decentralized energy systems using biomass resources for rural electrification in developing countries Diego