Energy security, uncertainty, and energy resource use option in

Ethiopia: A sector modelling approach

Dawit Guta and Jan Börner Center for Development Research (ZEF)

IEW/IRENA; Jun 3-5 2015, Abu Dhabi, UAE

1

Outline

Introduction Objective Methodology Results Conclusion Recommendations

2

Introduction

Energy source Unit Potential reserveExploited

Amount %

Hydroelectric GW 45 2.1 5%

Solar kWh/m2/day 4–6 ----

Wind GW 13.50 0.2

Introduction

Ethiopia’s percentage distribution of energy consumption by end-user, 2009 (IEA, 2009)

25%

61%

14%7%

0% 0%

99%

1%

92%

38% 38%

27%

1%2%4%

92%

1%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Industrial Transportation Residential Commercial andPublic services

Total

shar

e of

ene

rgy

Oil products Biofuel and wastes Electricty Total

4

Introduction

• Over 90% of population rely on traditional biomass energy for domestic purposes

• Homogenous electricity mix, reliance on hydroelectricity (90%)

• Steadily increasing electricity demand

• Economic growth outpacing the development of the energy sector

• vulnerability of energy sector to various uncertainties (drought and oil price shocks)

Energy development considered as core part of the Climate Resilient Growth Economy

(CRGE)

Long term power export plan

But high capital cost of alternative energy technologies

Energy sector model of optimal energy resource use, and technological alternatives

can help to evaluate future energy security

5

Objective

Objectives

1. To investigate least-cost energy source diversification option for Ethiopia

2. To estimate the impact on energy mix and cost of energy production of variousuncertainties and understand implication for future energy security

6

Methodology Energy sector model: Linear programing model using General Algebraic Modelling Systems (GAMS)

𝑚𝑚𝑖𝑖𝑖𝑖 𝐶𝐶 = �𝑡𝑡=1

𝑇𝑇

[ 1 + 𝜌𝜌 −𝑡𝑡 (𝑐𝑐𝑡𝑡𝑜𝑜 + 𝑐𝑐𝑡𝑡𝑘𝑘 + 𝑐𝑐𝑡𝑡𝑎𝑎)]

Where 𝐶𝐶 = the total discounted minimized cost, 𝜌𝜌 = discount rate,

𝑐𝑐𝑡𝑡𝑘𝑘 = ∑𝑖𝑖=1𝑛𝑛 ∑𝑗𝑗=1𝐽𝐽 ∑𝑡𝑡=1𝑇𝑇 𝑘𝑘𝑖𝑖𝑗𝑗𝑡𝑡 .𝑄𝑄𝑖𝑖𝑗𝑗𝑡𝑡 = total capital cost ,

𝑐𝑐𝑡𝑡𝑜𝑜 = ∑𝑖𝑖=1𝑛𝑛 ∑𝑗𝑗=1𝐽𝐽 ∑𝑡𝑡=1𝑇𝑇 𝑜𝑜𝑖𝑖𝑗𝑗𝑡𝑡 .𝑃𝑃𝑖𝑖𝑗𝑗𝑡𝑡 .∅𝒅𝒅, = total operating and management (O&M) costs ,

𝑐𝑐𝑡𝑡𝑎𝑎 = ∑𝑚𝑚=19 𝑟𝑟𝑏𝑏𝑚𝑚𝑡𝑡 .𝑄𝑄𝑏𝑏𝑚𝑚𝑡𝑡 + ∑𝑚𝑚=19 𝑟𝑟𝑠𝑠𝑚𝑚𝑡𝑡 .𝑄𝑄𝑠𝑠𝑚𝑚𝑡𝑡 = land rental cost

T = set of years from 2010 to 2110 𝑡𝑡 = time in years (𝑡𝑡 = 1,2, 3, … 𝑡𝑡)𝑖𝑖 = energy sources (𝑖𝑖 = 1, 2, … , 6,), hydropower, fossil thermal, geothermal, wind, solar, and biomass𝑗𝑗 = plant type (𝑗𝑗 = 1, 2, 3, … J) 𝑚𝑚 = region (m = 1, 2, 3,…9)∅𝒅𝒅 = duration of each electricity demand block in hours per yeard = blocks of electricity demand (peak, and off-peak)𝑘𝑘𝑖𝑖𝑗𝑗𝑡𝑡 = capital cost per MW, and 𝑜𝑜𝑖𝑖𝑗𝑗𝑡𝑡 = O&M cost per MWh/year,𝑟𝑟𝑏𝑏𝑚𝑚𝑡𝑡 = the land costs per MW, and 𝑟𝑟𝑠𝑠𝑚𝑚𝑡𝑡 = the land costs per tonne 𝑄𝑄𝑖𝑖𝑗𝑗𝑡𝑡 and 𝑃𝑃𝑖𝑖𝑗𝑗𝑡𝑡 energy output and installed capacity respectively 𝑄𝑄𝑠𝑠𝑚𝑚𝑡𝑡 and 𝑄𝑄𝑏𝑏𝑚𝑚𝑡𝑡 solid and electrical biomass capacities respectively 7

Result

Annual electricity demand projection: high growth rate of 9% and low growth rate of 6% (2010-2045); and 2.5% 2045-2110

42,547

113,000

-

20,000

40,000

60,000

80,000

100,000

120,00020

1520

2020

2520

3020

3520

4020

4520

5020

5520

6020

6520

7020

7520

8020

8520

9020

9521

0021

0521

10

Peak

ele

ctric

ity d

eman

d in

MW

High Low

8

Result

Energy production baseline: low demand growth rate

- 20,000 40,000 60,000 80,000

100,000 120,000 140,000 160,000 180,000 200,000

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

2075

2080

2085

2090

2095

2100

2105

2110

Ener

gy p

rodu

ctio

n in

GW

h

Thermal Biomass Geothermal

Wind Hydroelectric

9

Result

Energy production baseline: high demand growth rate

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

Ener

gy p

rodu

ctio

n in

GW

h

Thermal Solar BiomassGeothermal Wind Hydroelectric

10

Result

Cost competitiveness of renewable energy sources

Levelized cost of energy (LCoE) lowest for hydroelectric power and highest for

solar

0.05

0.08

0.10

0.12

0.19

0.11

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Hydroelectric Geothermal Wind Biomass Solar Average

LCoE

in U

S$/k

Wh

Levelized cost of energy (LCoE)

11

Result

Capital subsidy required to make alternative sources competitive with hydroelectric power

114.73

263.3

118.06 120.44

0

50

100

150

200

250

300

Wind Solar Geothermal Biomass

Subs

idy

in U

S$/K

w

Capital subsidy (US$/kW)

12

Result

Climate change scenarios, Hydroelectric power production (high demand growth rate)

→ may led to reduction in hydroelectric energy production in the long run (but

depends on electricity demand growth, and severity of drought)

→ Ethiopia needs to diversify to alternative expensive source

⇒ cost of energy production increases

- 20,000 40,000 60,000 80,000

100,000 120,000 140,000 160,000 180,000 200,000

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

2075

2080

2085

2090

2095

2100

2105

2110

Ener

gy in

GW

h pe

r yea

r

0.11 Min/Max0.11 Average0.25 Min/Max0.25 Average0.4 Min/Max0.4 AverageBase

13

Result

Climate change scenarios, cost of energy production (high electricity demand growth rate)

2

58

163

0

20

40

60

80

100

120

140

160

180

0.11 0.25 0.4

Cos

t in

US$

mill

ion

Standard deviation in water availability scenarios

14

Result

Effect of technological and efficiency innovation growth scenario, Hydroelectric power (high demand growth rate)

- 20,000 40,000 60,000 80,000

100,000 120,000 140,000 160,000 180,000 200,000

Ener

gy in

GW

h

Base

Low

Intermediate

Best

→reduction in cost of solar, wind and biomass

→ substitution for hydroelectric power

→ enhance energy security 15

Result

Shadow price of energy resources

0.000

0.001

0.002

0.003

0.004

0.005

0.006

Base Low Intermediate Best Average

Shad

ow p

rice

in U

S$/k

Wh

Hydroelectric Geothermal BiomassWind Average

→reduction in shadow price (scarcity) of energy resources

→ technological and efficiency innovations can be an engine of growth16

Result

Decrease in cost of energy production for different technological and efficiency innovation growth

-16-31.2

-77.4-83.7

-192.7

-419-450

-400

-350

-300

-250

-200

-150

-100

-50

0Low Intermediate Best

Cos

t in

US$

mill

ion

Low demand growthHigh demand growth

17

Conclusion

Reliance on hydroelectric power may increase the risk of vulnerability to climate change

uncertainty in the long run

→the country needs to diversify to expensive resources

→ this increases cost of energy production

Technological and efficiency innovations are key for reducing the risks posed on

hydroelectric reservoir due to climate change uncertainty

→decrease in cost of energy production

→substitution for drought vulnerable hydroelectric power

→decrease in shadow price of energy resources

⇒ enhances energy security and creates economic growth opportunity

18

Recommendation

Closing technical, financial, and efficiency gaps that exist in the country’s energy sector

Strategies for promoting technological and efficiency innovation

→ promoting R&D

→ local technological capability building

→ human skill development (learning and adaptability)

→ Innovative clean energy financing approaches (capital subsidies)

Integrating afforestation and reforestation initiatives with watershed management

→reduce reservoir siltation risks and enhance hydroelectric power generation

19

Acknowledgements

DAAD

Fiat Panis foundation

BMZ

Thank you!

Slide Number 1OutlineIntroductionIntroductionIntroduction ObjectiveMethodology ResultResultResultResult ResultResultResultResultResult Result Conclusion Recommendation AcknowledgementsSlide Number 21