REPERCUSSION OF LARGE SCALE HYDRO DAM DEPLOYMENT: …

REPERCUSSION OF LARGE SCALE HYDRO DAM

DEPLOYMENT: THE CASE OF CONGO GRAND INGA HYDRO

PROJECT

Ayobami S. Oyewo, Javier Farfan Orozco and Christian Breyer Lappeenranta University of Technology, Finland

Neo-Carbon Energy 8th Researchers’ Seminar, Lappeenranta, August 23-24, 2017

Repercussion of large scale hydro dam deployment: The case of Congo Grand Inga hydro projectSolomon Oyewo [email protected]

Agenda

IntroductionMethodology and DataScenario AssumptionsResultsSummary


IntroductionAfrica needs clean, consistent and cost-effective energy supply to meet the currentenergy deficit and future demand.

One of the proposed response to address the energy challenges and persistent infrastructural gaps is to significantly increase investment in large hydropower dams.

Proponents of very large dams have often exaggerated potential multiple benefits of a mega dam, marginalize environmental concerns and neglect the true risk of such projects, in particular for the fragile economies of developing countries

Studies have reported the financial risks, cost overruns and schedule spills associated with very large dams.

In this study scenarios are defined based on announced costs and expected costs. Cost escalations in the range from additional 5% to 100% for the Inga project in 2030 and 2040 are considered, as average cost overruns are typically at about 70% or higher.


Inga project development

Figure 1: Congo River and all Inga plans (existing and planned phases of Inga dams)

The Inga Rapids have been targeted for hydropower development since 1885.The hydropower potential is estimated to be about 40 GW.The project is divided into eight dams and seven phases (Fig. 1).The Grand Inga project is estimated to be over 80 bUSD.Currently, Inga 1 and 2, are undergoing intensive rehabilitation.Both dams operate only 40% of their nameplate capacity.


Issues on Inga project developmentFinancial issues

• The first phase of Inga (Inga 1 and 2), was built disregarding the feasibility study that found both projects to be uneconomical at that time.

• Inga 1 and 2 contribute to half of DRC’s current external debt, while the Inga-Kolwezi transmission line account for the biggest share of DRC’s debt problem during 1990s.

• The rehabilitation of Inga 1 and 2, has received funds from international bodies yet suffer delay of complete renovation, in total the rehabilitation cost skyrocketed to 1.2 bUSD, six times the initial estimate.

• On the 25th of July, 2016 the World Bank announced suspension of funding Inga 3 base chute project.

Environmental Issues• Damming of Congo River would affect the local environment, regional ecosystem and global climate.• The ‘Congo Plume’, which represents the one of the largest carbon sink in the world, can be disrupted by additional

dams, hence contributing to global warming. • Rare species of fish, plants and animals are at risk of being affected, or in an extreme case might be in danger of

extinction (estimates are around 500 endemic species at risk).• Flooding of the Bundi Valley to create a reservoir can lead to huge methane emissions and disease outbreaks.

Social Issues• For instance, the power harnessed from Grand Inga is not envisioned for supplying electricity to domestic users or

increase electricity access to rural areas.• Displacement of people whose livelihood depends on the River.


Current status of the power plant mix

Key insights:PV and wind in very early growth phaseCurrent power system is dominated by fossil based fuel, in particular large and old coal capacities.Increasing gas capacities in last 10 years.

source: Farfan J. and Breyer Ch., 2017. Structural changes of global power generation capacity towards sustainability and the risk of stranded investments supported by a sustainability indicator; J of Cleaner Production, 141, 370-384


Agenda


88 Repercussion of large scale hydro dam deployment: The case of Congo Grand Inga hydro projectSolomon Oyewo [email protected]

Methodology – Modelling ObjectiveDefinition of an optimally structured energy system based on 100% RE supply

optimal set of technologies, best adapted to the availability of the regions’ resources,optimal mix of capacities for all technologies,optimal operation modes for every element of the energy system, least cost energy supply for the given constraints.

LUT Energy model, key featureslinear optimization modelhourly temporal resolution0.45° x 0.45° spatial resolution multi-node approachflexibility and expandability

Input datahistorical weather data for: solar irradiation, wind speed and hydro precipitationavailable sustainable resources for biomass and geothermal energysynthesized power load datagas and water desalination demandefficiency/ yield characteristics of RE plantsefficiency of energy conversion processescapex, opex, lifetime for all energy resourcesmin and max capacity limits for all RE resourcesnodes and interconnections configuration

99 Repercussion of large scale hydro dam deployment: The case of Congo Grand Inga hydro projectSolomon Oyewo [email protected]

MethodologyFull system

Renewable energy sources• PV rooftop• PV ground-mounted• PV single-axis tracking • Wind onshore• Hydro run-of-river• Hydro dam• Geothermal energy• CSP• Waste-to-energy• Biogas• Biomass

Electricity transmission• node-internal AC transmission• interconnected by HVDC lines

Storage options• Batteries • Pumped hydro storage• Adiabatic compressed air storage• Thermal energy storage, Power-to-Heat• Gas storage based on Power-to-Gas

• Water electrolysis• Methanation• CO2 from air• Gas storage

Energy Demand• Electricity• Industrial Gas


Agenda



Scenarios assumptions

Figure 5. Sub-Saharan African sub-regions and HVDC transmission lines configuration.

16 regionsAfrica West West Africa West SouthAfrica West North2 regions in Nigeria (North and South)Africa CentralDRC CongoSudan EritreaEthiopiaSomalia DjiboutiKenya UgandaTanzaniaAfrica South EastAfrica South WestSouth AfricaIndian Ocean

Key Insights:~1383 and 1800 mio population in 2030 and 2040 respectively.~866 and 1483 TWh electricity demand in 2030 and 2040 respectively. ~25 mio km2 area

source:Barasa M., et al., 2016. A Cost Optimal Resolution for Sub-Saharan Africa powered by 100% Renewable for year 2030 Assumptions, 32nd European Photovoltaic Solar Energy Conference, Munich, June 20–24


Scenario assumptionsGeneration profile

Solar PV single-axis tracking (left) and wind power (right) generation profiles for SSA

Key Insights:PV resources available all through the yearGood wind resources during the rainy season and nights to supplement the absence solar and reduces dependence on storage


Scenario formulationA range of scenarios has been formulated, in order to analyse the impact of the Grand Inga project on the SSA power system. To achieve the aim of this paper, categories of scenarios were formulated based on announced and overnight (expected) cost assumptions

All scenarios were be analysed from a scope of assumptions for the years 2030 and 2040 from an evolutionary perspective.

Scenario options are as follows:• No Inga scenario: In this scenario Inga 1 and 2, were considered (Reference scenario)• Optional Inga 3 Scenario: In this scenario any Grand Inga deployment after Inga 3 is not

considered. Two scenarios were formulated from this category; Inga 3 announced and expected cost.

• Forced Inga 3 and Grand Inga: In this scenario Inga 3 is forced into operation, as well as subsequent phases of the Grand Inga project. Grand Inga (GI) scenario were formulated in cost escalation range from 0-100% was examined. GI 0%, 50% and 100% scenarios were selected for this presentation.


Agenda



ResultsTotal annual cost in [b€] for GI 2030 Scenarios

Total annual cost in [b€] for GI 2030 Scenarios

Key Insights:An inflection point slightly over 35% was found for 2030 assumptions.This clearly shows that with cost overrun is up to 40% for the year 2030, the project is economically beneficial.


ResultsTotal annual cost in [b€] for GI 2040 Scenarios

Total annual cost in [b€] for GI 2040 Scenarios

Key Insights:By 2040, the inflection point dropped below zero, with reference at -5%.Which implies that with cost overrun up to 0% the project is economically beneficial by 2040


ResultsScenario I3 announced cost in absolute total LCOE (left) and relative difference to the reference scenario (right) for the years 2030 (top) and 2040 (bottom).

Key Insights:Based on regional average LCOE impact of Inga 3 is negligible in both 2030 and 2040 cases.

The relative percentage differences in LCOE range from -3.2% to 0.8% in 2030 and from -2.0% to 0.3% in 2040, across the regions.

While the overall regional relative averages are -0.4% and -0.1%, for 2030 and 2040, respectively.

The overall average LCOE is 54.1 €/MWh in 2030 and 41.7 €/MWh in 2040 as shown in the top left and bottom left figures, respectively


ResultsScenario GI 100% in absolute total LCOE (left) and relative difference to the reference scenario (right) for the years 2030 (top) and 2040 (bottom).

Key Insights:The relative percentage differences in LCOE range from 0% to 42.2% % in 2030 and from -7.8% to 46.8% in 2040, across the regions.

In this scenario, the DRC will experience 42.2% and 46.8% increase in LCOE, by 2030 and 2040, respectively..

While the overall regional relative LCOE average increased by 2.0% in 2030 and 1.4% in 2040.


ResultsThe +35% and -5% scenarios (near inflection point) in absolute total LCOE (left) and relative difference to the reference scenario (right) for the years 2030 (top) and 2040 (bottom).

Key Insights:In both scenarios the overall relative LCOE average was 0.0% for the entire region. Yet, DRC LCOE increased by 24.0% and 14.7%, in Grand Inga 35% and -5%, respectively.

According to this scenario, DRC still experience an increase in LCOE; however, the impact is still negligible.

In the adjacent regions the highest decrease was by 4% in 2030 and 1.4% in 2040, and a similar occurrence was noticed in regions in the West and East.

Conversely, for Somalia LCOE decreased by 11.5% in 2030 and 7.8% in 2040.


ResultsFinancial results for the scenarios applied in SSA by 2030. Abbreviations: Inga 3 (I3) and Grand Inga (GI).


ResultsEnergy flow of the system for the GI 0% scenarios for the year 2040.

Key insights:PV dominates the electricity generation, Substantial part of demand is stored in batteries


Agenda



SummaryRegarding LCOE, a decline occurred in LCOE in 2040 when compared to 2030 in all the scenarios examined; for instance, at the inflection point (near +35% of the announced cost for 2030 and -5% of the announced cost for 2040), the point at which the relative difference LCOE is zero for the entire region, is 54.4 €/MWh in 2030 and 41.7 €/MWh in 2040.

while the LCOE obtained for 100% GI cost overrun in the DRC region is 68.4 €/MWh in 2030 and 60.4 €/MWh in 2040. These are 79.5% and 69%% higher than the reference averages, respectively.

The presented detailed cost analysis for SSA clearly reveals that it is highly unlikely that future Inga hydropower capacity expansions can compete with solar PV and wind energy, in particular taking into account not just the planned, but the expected investment cost.

By 2040, the result of this research reveals that solar PV dominates in terms of installed capacities, due to its fast development and continuous cost decline.

The predominant role of solar PV and battery storage, due to highly favourable economics, was observed in this work. SSA can mainly be powered by solar PV and complemented by wind energy.

Utility-scale RE technologies, like solar PV and wind energy, have the potential to meet the energy demand sustainably and are also becoming increasingly cost-effective in SSA.

Beyond the financial risk that can be incurred from building the GI dam, severe environment disruptions would be caused from diverting river flow to create the required reservoir.

THANK YOU FOR YOUR ATTENTION

The authors gratefully acknowledge the public financing of Tekes, theFinnish Funding Agency for Innovation, for the ‘Neo-Carbon Energy’project under the number 40101/14. The first author would like to thankLUT Foundation for the valuable scholarship


ResultsScenario I3 expected cost in absolute total LCOE (left) and relative difference to the reference scenario (right) for the years 2030 (top) and 2040 (bottom).

Key Insights:The relative percentage differences in LCOE range from -3.5% to 18.4 % in 2030 and from -7.0% to 11.5 % in 2040, across the regions.

Most of the cost burden is bore by DRC as LCOE increase by 18.4% and 11.5% in 2030 and 2040 respectively.

The overall regional increased averages by 0.2% and 0.3%, for 2030 and 2040, respectively.

The overall average LCOE is 54.5 €/MWh in 2030 and 41.9 €/MWh in 2040 as shown in the top left and bottom left figures, respectively



Key Insights:The relative percentage differences in LCOE range from -11% to 12.9 % in 2030 and from -7.5% to 16.1 % in 2040, across the regions.

For the year 2030, LCOE increased from 10-12% in DRC and Southern and Northern neighbouring regions. While huge benefit is received by Somalia (with -11.2) and Tanzania (-6.1%).

By 2040, LCOE increased in the host country by 16.1%.

The overall regional averages is -0.7% and 0.1%, for 2030 and 2040, respectively.



Key Insights:The relative percentage differences in LCOE range from -11% to 29% % in 2030 and from -7.8% to 31.1% in 2040, across the regions.

Similar to previous scenario, LCOE declined by -11% in 2030 and -7.8% in 2040 in Somalia and Djibouti, while DRC experience LCOE increase by 29% and 31% in 2030 and 2040, respectively.

In this scenario the LCOE is 62.0 €/MWh in 2030 and 53.7 €/MWh in 2040 in the DRC

In this scenario, DRC bears a tremendous cost burden, while the LCOE decrease in most region was minimal, except for Somalia, in both years.


Financial and technical assumptions 2030 2040

Technology Capex [€/kW]

Opex fix [€/(kW a)]

Opex var [€/kWh]

Lifetime [a]

Capex [€/kW]

Opex fix [€/(kW a)]

Opex var [€/kWh]

Lifetime [a]

PV optimally tilted 550 8 0 35 370 5.6 0 40 PV single-axis tracking 620 9 0 35 410 6.2 0 40 PV rooftop 813 12 0 35 610 9.2 0 40 Wind onshore 1000 20 0 25 940 18.8 0 25 CSP (solar field) 528 11 0 25 528 11 0 25 Hydro run-of-river 2560 115.2 0.005 60 2560 76.8 0.005 50 Hydro dam 1650 66 0.003 60 1650 49.5 0.003 50 Geothermal energy 4860 87 0 30 4441 88.9 0 40 Water electrolysis 380 13 0.0012 30 310 12.4 0.0012 30 Methanation 234 5 0.0015 30 190 3.8 0.0015 30 CO2 scrubbing 356 14 0.0013 30 286 11.4 0.0013 30 CCGT 775 19.4 0.001 30 775 19.4 0.001 35 OCGT 475 14.25 0.001 30 475 14.3 0.001 35 Steam turbine 700 14 0 30 640 12.8 0 30 Hot heat burner 100 2 0 30 100 2 0 30 Heating rod 20 0.4 0.001 30 20 0.4 0.001 30 Biomass CHP 2500 175 0.001 30 2500 175 0.001 30 Biogas CHP 370 14.8 0.001 30 325 13 0.001 30 Waste incinerator 5240 235.8 0.007 20 4870 219.2 0.0069 30 Biogas digester 680 27.2 0 20 632 25.3 0 25 Biogas upgrade 250 20 0 20 220 17.6 0 20

Capex

[€/kWh] Opex fix

[€/(kWh a)] Opex var [€/kWh]

Lifetime [a]

Capex [€/kWh]

Opex fix [€/(kWh a)]

Opex var [€/kWh]

Lifetime [a]

Battery 150 10 0.0002 10/20 100 2.5 0.0002 20 PHS 70 11 0.0002 50 70 11 0.0002 50 A-CAES 31 0.4 0.0012 40 29.75 0.39 0.0012 55 TES 24 2 0 20 20 0.3 0 30 Gas storage 0.05 0.001 0 50 0.05 0.001 0 50

Capex [€/(kWNTC

*km)]

Opex fix [€/(kWNTC

km a)]

Opex var [€/kWhNTC]

Lifetime [a]

Capex [€/(kWN

TC*km)]

Opex fix [€/(kWNTC k

a)]

Opex var [€/kWhNT

C]

Lifetime [a]

HVDC line on ground 0.612 0.0075 0 50 1.044 0.003 0 50 HVDC line submarine 0.992 0.0010 0 50 0.992 0.0010 0 50 HVDC converter pair 180 1.8 0 50 180 1.8 0 50


Efficiency assumptions for energy system components for the reference year

2030 2040

Technology Efficiency [%]

Energy/Power Ratio [h]

Self-Discharge [%/h] Efficiency [%] Energy/Power

Ratio [h] Self-Discharge

[%/h] Battery 90 6 0 97 6 0 PHS 85 8 0 92 8 0 A-CAES 70 100 0.001 83 100 0.001 TES 90 8 0.002 90 8 0.002 Gas storage 100 80*24 0 100 80*24 0

2030 2040

el [%] th [%] el [%] th [%]

CSP (solar field) 51 51 Steam turbine 42 43 Hot heat burner 95 95 Heating rod 99 99 Water electrolysis 84 84 Methanation 77 77 CO2 scrubbing 78 78 CCGT 58 60 OCGT 43 43 Geothermal 24 24 Biomass CHP 40 45 40 47 Biogas CHP 42 43 42 43 Waste incinerator 34 37 Biogas upgrade 98 98

Power losses HVDC line 1.6 % / 1000 km HVDC converter pair 1.4%


Electricity Price (in €/MWh)

2030

2040

Region Residential Commercial Industry Residential Commercial Industry West-West 180 184 187 221 224 226 West-South 170 186 203 217 241 265 West-North 176 162 148 225 210 194 South Nigeria 46 81 117 74 119 163 North Nigeria 46 81 117 74 119 163 Sudan Eritrea 167 124 81 225 178 130 Ethiopia 180 144 107 242 199 155 Somalia 185 151 117 249 207 165 Kenya Uganda 218 241 195 270 312 263 Tanzania 148 156 164 200 210 220 Central 157 165 173 212 222 233 Congo 73 114 155 119 164 209 South-West 113 96 78 163 139 115 South Africa 71 57 43 114 92 70 South-East 120 122 124 168 170 172 Indian Ocean 135 132 130 177 173 170


Overview of prosumer electricity costs, installed capacities and energy utilization for SSA, in 2030 (top) and 2040 (bottom)

Prosumers parameters Residential Commercial Industrial Electricity price [€/kWh] 0.0929 0.0926 0.0899 PV LCOE [€/kWh] 0.0255 0.0341 0.0339 Self-consumption PV LCOE [€/kWh] 0.0302 0.0387 0.0380 Self-consumption PV and Battery LCOE [€/kWh] 0.0569 0.0625 0.0590 Self-consumption LCOE [€/kWh] 0.0566 0.0622 0.0588 Benefit [€/kWh] 0.0363 0.0304 0.0311 Installed capacities Residential Commercial Industrial PV [GW] 23.5 13.8 24.0 Battery storage [GWh] 31.8 15.7 24.7 Generation Residential Commercial Industrial PV [TWh] 40.2 23.6 41.3 Battery storage [TWh] 10.3 5.1 7.9 Excess [TWh] 5.0 2.3 3.5 Utilization Residential Commercial Industrial Self-consumption of generated PV electricity [%] 84.7 88.0 89.4 Self-coverage market segment [%] 32.9 22.8 44.3 Self-coverage operators [%] 13.5 11.9 10.9

Prosumers parameters Residential Commercial Industrial Electricity price [€/kWh] 0.1502 0.1551 0.1561 PV LCOE [€/kWh] 0.0258 0.0343 0.0339 Self-consumption PV LCOE [€/kWh] 0.0318 0.0415 0.0408 Self-consumption PV and Battery LCOE [€/kWh] 0.0475 0.0606 0.0588 Self-consumption LCOE [€/kWh] 0.0472 0.0602 0.0585 Benefit [€/kWh] 0.1030 0.0949 0.0975 Installed capacities Residential Commercial Industrial PV [GW] 72.3 47.1 78.9 Battery storage [GWh] 157.4 105.5 169.4 Generation Residential Commercial Industrial PV [TWh] 121.9 80.2 135.9 Battery storage [TWh] 49.2 33.0 53.4 Excess [TWh] 18.5 10.9 18.5 Utilization Residential Commercial Industrial Self-consumption of generated PV electricity [%] 81.3 82.7 83.0 Self-coverage market segment [%] 34.8 23.3 42.0 Self-coverage operators [%] 24.6 24.6 23.2


Upper limits of installable capacities in SSA regions in units of GWth for CSP and GWel for all other technologies.


Lower limits of installed capacities in SSA regions


Financial and technical assumptions

Regional biomass and geothermal energy potentialsRegional biomass costs, calculated based on mix of biomass sources in the region


Overview of storage capacities, throughput, and full cycles per year for the four scenarios for Sub-Saharan Africa in 2030 (top) and 2040 (bottom)

REPERCUSSION OF LARGE SCALE HYDRO DAM DEPLOYMENT: …

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