REPERCUSSION OF LARGE SCALE HYDRO DAM DEPLOYMENT: THE CASE OF CONGO GRAND INGA HYDRO PROJECT

Transcription

1 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

2 Agenda Introduction Methodology and Data Scenario Assumptions Results Summary 2

3 Introduction Africa needs clean, consistent and cost-effective energy supply to meet the current energy 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. 3

4 Inga project development The Inga Rapids have been targeted for hydropower development since 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. Figure 1: Congo River and all Inga plans (existing and planned phases of Inga dams) 4

5 Issues on Inga project development Financial 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 25 th 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. 5

6 Current status of the power plant mix Key insights: PV and wind in very early growth phase Current 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., 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,

7 Agenda Introduction Methodology and Data Scenario Assumptions Results Summary 7

8 Methodology Modelling Objective Definition 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. Input data historical weather data for: solar irradiation, wind speed and hydro precipitation available sustainable resources for biomass and geothermal energy synthesized power load data gas and water desalination demand efficiency/ yield characteristics of RE plants efficiency of energy conversion processes capex, opex, lifetime for all energy resources min and max capacity limits for all RE resources nodes and interconnections configuration LUT Energy model, key features linear optimization model hourly temporal resolution 0.45 x 0.45 spatial resolution multi-node approach flexibility and expandability 88

9 99 Methodology Full 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 CO 2 from air Energy Demand Electricity Industrial Gas Repercussion Gas storage of large scale hydro dam deployment: The case of Congo Grand Inga hydro project

10 Agenda Introduction Methodology and Data Scenario Assumptions Results Summary 10

11 Scenarios assumptions 16 regions Africa West West Africa West South Africa West North 2 regions in Nigeria (North and South) Africa Central DRC Congo Sudan Eritrea Ethiopia Somalia Djibouti Kenya Uganda Tanzania Africa South East Africa South West South Africa Indian 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 km 2 area source: Barasa M., et al., A Cost Optimal Resolution for Sub-Saharan Africa powered by 100% Renewable for year 2030 Assumptions, 32nd European Photovoltaic Solar Energy Conference, Munich, June Figure 5. Sub-Saharan African sub-regions and HVDC transmission lines configuration. 11

12 Scenario assumptions Generation profile Solar PV single-axis tracking (left) and wind power (right) generation profiles for SSA Key Insights: PV resources available all through the year Good wind resources during the rainy season and nights to supplement the absence solar and reduces dependence on storage 12

13 Scenario formulation A 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. 13

14 Agenda Introduction Methodology and Data Scenario Assumptions Results Summary 14

15 Results Total 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. 15

16 Results Total 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

17 Results Scenario 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 17

18 Results Scenario 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

19 Results The +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

20 Results Financial results for the scenarios applied in SSA by Abbreviations: Inga 3 (I3) and Grand Inga (GI). 20

21 Results Energy flow of the system for the GI 0% scenarios for the year Key insights: PV dominates the electricity generation, Substantial part of demand is stored in batteries 21

22 Agenda Introduction Methodology and Data Scenario Assumptions Results Summary 22

23 Summary Regarding 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 while the LCOE obtained for 100% GI cost overrun in the DRC region is 68.4 /MWh in 2030 and 60.4 /MWh in 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. 23

24 THANK YOU FOR YOUR ATTENTION The authors gratefully acknowledge the public financing of Tekes, the Finnish Funding Agency for Innovation, for the Neo-Carbon Energy project under the number 40101/14. The first author would like to thank LUT Foundation for the valuable scholarship

25 Results Scenario 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 25

26 Results Scenario GI 0% 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 -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. 26

27 Results Scenario GI 50% 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 -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. 27

28 Financial and technical assumptions Technology Capex Opex fix Opex var Lifetime Capex Opex fix Opex var Lifetime [ /kw] [ /(kw a)] [ /kwh] [a] [ /kw] [ /(kw a)] [ /kwh] [a] PV optimally tilted PV single-axis tracking PV rooftop Wind onshore CSP (solar field) Hydro run-of-river Hydro dam Geothermal energy Water electrolysis Methanation CO2 scrubbing CCGT OCGT Steam turbine Hot heat burner Heating rod Biomass CHP Biogas CHP Waste incinerator Biogas digester Biogas upgrade Capex [ /kwh] Opex fix [ /(kwh a)] Opex var [ /kwh] Lifetime [a] Capex [ /kwh] Opex fix [ /(kwh a)] Opex var [ /kwh] Lifetime [a] Battery / PHS A-CAES TES Gas storage Capex Opex fix [ /(kwntc km a)] Opex var [ /kwhntc] Lifetime [a] Capex Opex fix [ /(kwntc k a)] Opex var [ /(kwntc *km)] [ /(kwn TC*km)] [ /kwhnt C] HVDC line on ground HVDC line submarine HVDC converter pair Lifetime [a] 28

29 Efficiency assumptions for energy system components for the reference year Technology Efficiency Energy/Power Self-Discharge Energy/Power Self-Discharge Efficiency [%] [%] Ratio [h] [%/h] Ratio [h] [%/h] Battery PHS A-CAES TES Gas storage * * el [%] th [%] el [%] th [%] CSP (solar field) Steam turbine Hot heat burner Heating rod Water electrolysis Methanation CO2 scrubbing CCGT OCGT Geothermal Biomass CHP Biogas CHP Waste incinerator Biogas upgrade Power losses HVDC line 1.6 % / 1000 km HVDC converter pair 1.4% 29

30 Electricity Price (in /MWh) Region Residential Commercial Industry Residential Commercial Industry West-West West-South West-North South Nigeria North Nigeria Sudan Eritrea Ethiopia Somalia Kenya Uganda Tanzania Central Congo South-West South Africa South-East Indian Ocean

31 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] PV LCOE [ /kwh] Self-consumption PV LCOE [ /kwh] Self-consumption PV and Battery LCOE [ /kwh] Self-consumption LCOE [ /kwh] Benefit [ /kwh] Installed capacities Residential Commercial Industrial PV [GW] Battery storage [GWh] Generation Residential Commercial Industrial PV [TWh] Battery storage [TWh] Excess [TWh] Utilization Residential Commercial Industrial Self-consumption of generated PV electricity [%] Self-coverage market segment [%] Self-coverage operators [%] Prosumers parameters Residential Commercial Industrial Electricity price [ /kwh] PV LCOE [ /kwh] Self-consumption PV LCOE [ /kwh] Self-consumption PV and Battery LCOE [ /kwh] Self-consumption LCOE [ /kwh] Benefit [ /kwh] Installed capacities Residential Commercial Industrial PV [GW] Battery storage [GWh] Generation Residential Commercial Industrial PV [TWh] Battery storage [TWh] Excess [TWh] Utilization Residential Commercial Industrial Self-consumption of generated PV electricity [%] Self-coverage market segment [%] Self-coverage operators [%]

32 Upper limits of installable capacities in SSA regions in units of GW th for CSP and GW el for all other technologies. 32

33 Lower limits of installed capacities in SSA regions 33

34 Financial and technical assumptions Regional biomass costs, calculated based on mix of biomass sources in the region Regional biomass and geothermal energy potentials 34

35 Overview of storage capacities, throughput, and full cycles per year for the four scenarios for Sub-Saharan Africa in 2030 (top) and 2040 (bottom) 35