Selected ‘Starter kit’ energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

doi:10.21203/rs.3.rs-1178306/v1

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Selected ‘Starter kit’ energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

https://doi.org/10.21203/rs.3.rs-1178306/v1

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Energy system modelling can be used to develop internally consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment, the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modelling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modelling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Energy Engineering

U4RIA

Renewable energy

Cost-optimization

Energy policy

OSeMOSYS

clicSAND

Can be used to develop national energy system models to inform national energy investment outlooks and policy plans and provide insights on the evolution of the electricity supply system under different trajectories.
Useful for country energy system analysts, policymakers and the broader scientific community as a zero-order starting point for model development.
Can be used to examine a range of possible energy system pathways, in addition to the case studies given in this study, to provide a further understanding of the evolution of the country's power system.
Useful for analysing the power system but also for capacity building activities. The methodology of translating the input data into modelling assumptions for a cost-optimization tool is presented in the appendix, which helps develop a zero-order Tier 2 national energy model (1, source A) consistent with U4RIA energy planning goals (2).
Useful for accelerating teaching activities, consultations, and government policy analysis in the energy planning field as evidenced by research that has been based on these data, including assessment of wind power in Morocco (3), assessment of NDC targets in Ghana (4), and assessment of decarbonisation pathways in Kenya (5).
By combining secondary data from multiple, diverse sources, the work provides analysts with complete and accessible datasets, helping to overcome barriers of data inaccessibility.

The data provided can be used as input data to develop an energy system model for the included countries in Africa, South America, and Asia. These countries are selected based on geography and data availability. This paper presents selected country-specific data and related aggregated data by region, with an example energy system model in the appendix; however, additional more comprehensive country-specific datasets are available externally for each country (see Appendix B for links to each available country-specific dataset, which should be consulted by those wishing to use these data for their own country analyses). As an illustration, these data were used to develop an example energy system model for Kenya using the cost-optimization tool OSeMOSYS (6) for 2015-2050. For reference, that model is described in Appendix A, and its data files are available as supplementary materials. The data provided were collected from publicly available sources, including the reports of international organizations, journal articles and existing model databases. The data sources used are listed in Table 1; each data source is assigned a letter code which is then referred to in the text. The dataset includes the techno-economic parameters of supply-side technologies, installed capacities, emissions factors and final electricity demands.

U4RIA are practical goals designed to improve energy modelling for policy support through guidelines and best practices (2). They are short for Ubuntu (meaning community focused), Retrievability, Reusability, Repeatability, Interoperability and Auditability. The datasets and example model move to meet U4RIA goals in that partially:

We develop examples of results that can be used by other research communities, including energy and transport, and to aid mitigation strategies.
The illustrative analyses are retrievable, reusable, repeatable.
As data are defined, elements of interoperability are feasible.
Moreover, the investigation could be audited or verified (not to say that it is ‘accurate’ but simply reproducible).

Item	Description of Content
Table 1	A table showing the raw data sources that data were collected from
Table 2	A table showing the estimated installed capacity of different on-grid power plant types in selected countries in Africa in 2018
Table 3	A table showing the estimated installed capacity of different on-grid power plant types in selected countries in East Asia in 2018
Table 4	A table showing the estimated installed capacity of different on-grid power plant types in selected countries in South America in 2018
Table 5	A table showing the estimated installed capacity of off-grid solar PV and hydropower in selected countries in Africa in 2018
Table 6	A table showing the estimated installed capacity of off-grid solar PV and hydropower in selected countries in East Asia in 2018
Table 7	A table showing the estimated installed capacity of off-grid solar PV and hydropower in selected countries in East South America in 2018
Table 8	A table showing techno-economic parameters for electricity generation technologies in Africa
Table 9	A table showing techno-economic parameters for electricity generation technologies in South East Asia
Table 10	A table showing techno-economic parameters for electricity generation technologies in South America
Table 11	A table showing capital cost projections for renewable energy technologies in Africa up to 2050
Table 12	A table showing capital cost projections for renewable energy technologies in South East Asia up to 2050
Table 13	A table showing capital cost projections for renewable energy technologies in South America up to 2050
Table 14	A table showing estimated average capacity factors for solar PV, hydropower and wind in selected countries in Africa
Table 15	A table showing estimated average capacity factors for solar PV, hydropower and wind in selected countries in East Asia
Table 16	A table showing estimated average capacity factors for solar PV, hydropower and wind in selected countries in South America
Table 17	A table showing estimated combined efficiency of transmission and distribution in selected countries in Africa in 2020, 2030 & 2050
Table 18	A table showing estimated combined efficiency of transmission and distribution in selected countries in East Asia in 2020, 2030 & 2050
Table 19	A table showing estimated combined efficiency of transmission and distribution in selected countries in South America in 2020, 2030 & 2050
Table 20	A table showing estimated domestic refinery capacity for selected countries in Africa
Table 21	A table showing estimated domestic refinery capacity for selected countries in East Asia
Table 22	A table showing estimated domestic refinery capacity for selected countries in South America
Table 23	A table showing cost and performance data for refinery technologies
Table 24	A table showing fuel price projections in Africa up to 2050
Table 25	A table showing fuel price projections in East Asia up to 2050
Table 26	A table showing fuel price projections in South America up to 2050
Table 27	A table showing carbon dioxide emissions factors by fuel
Table 28	A table showing estimated renewable energy potentials for selected countries in Africa
Table 29	A table showing estimated renewable energy potentials for selected countries East in Asia
Table 30	A table showing estimated renewable energy potentials for selected countries in South America
Table 31	A table showing estimated fossil fuel reserves for selected countries in Africa
Table 32	A table showing estimated fossil fuel reserves for selected countries in Asia
Table 33	A table showing estimated fossil fuel reserves for selected countries in South Africa
Figure 1	A graph showing a final electricity demand projection for selected countries in the North Africa Power Pool from 2015-2050
Figure 2	A graph showing a final electricity demand projection for selected countries in the Central Africa Power Pool from 2015-2050
Figure 3	A graph showing a final electricity demand projection for selected countries in the East Africa Power Pool from 2015-2050
Figure 4	A graph showing a final electricity demand projection for selected countries in the West Africa Power Pool from 2015-2050
Figure 5	A graph showing a final electricity demand projection for selected countries in the South Africa Power Pool from 2015-2050
Figure 6	A graph showing a final electricity demand projection for selected countries in East Asia from 2015-2050
Figure 7	A graph showing a final electricity demand projection for selected countries in South America from 2015-2050

Table 1: Data sources used in this article. In the text, lettered data sources corresponding to those in Table 1 are included in brackets

Source ID	Reference
A	C. Cannone, Towards evidence-based policymaking: energy modelling tools for sustainable development, UPC Barcelona (2020). http://hdl.handle.net/2117/333306
B	M. Brinkerink, P. Deane, PLEXOS-World 2015. (2020). https://doi.org/10.7910/DVN/CBYXBY
C	M. Brinkerink, B. Gallachóir, P. Deane, Building and Calibrating a Country-Level Detailed Global Electricity Model Based on Public Data, Energy Strateg Rev. 33 (2021) 100592. https://doi.org/10.1016/j.esr.2020.100592
D	L. Byers, J. Friedrich, R. Hennig, A. Kressig, X. Li, C. McCormick, et al., A Global Database of Power Plants, Washington, DC (2018). https://www.wri.org/publication/global-power-plant-database
E	IRENA, Renewable Energy Statistics 2020, Abu Dhabi (2020). https://www.irena.org/publications/2020/Jul/Renewable-energy-statistics-2020
F	IRENA, Planning and Prospects for Renewable Power in Eastern and Southern Africa, Abu Dhabi (2021). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2021/Apr/IRENA_Planning_Prospects_Africa_2021.pdf
G	IRENA, Planning and prospects for renewable power: West Africa, Abu Dhabi (2018). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Nov/IRENA_Planning_West_Africa_2018.pdf
H	IRENA, Future of Wind, Abu Dhabi (2019). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf
I	IRENA, ASEAN Centre for Energy, Renewable Energy Outlook for ASEAN, Abu Dhabi (2016). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2016/IRENA_REmap_ASEAN_2016_report.pdf
J	IRENA, Renewable Power Generation Costs in 2019, Abu Dhabi (2020). https://irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jun/IRENA_Power_Generation_Costs_2019.pdf
K	G. N. P. de Moura, L.F.L. Legey, M. Howells, A Brazilian perspective of power systems integration using OSeMOSYS SAMBA – South America Model Base – and the bargaining power of neighbouring countries: A cooperative games approach, Energy Policy. 115 (2018) 470–485. https://doi.org/10.1016/j.enpol.2018.01.045
L	I. Staffell, S. Pfenninger, Using bias-corrected reanalysis to simulate current and future wind power output, Energy. 114 (2016) 1224–1239. https://doi.org/10.1016/j.energy.2016.08.068
M	I. Staffell, S. Pfenninger, Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy. 114 (2016). 1251–1265. https://doi.org/10.1016/j.energy.2016.08.060
N	I. Pappis, V. Sridharan, W. Usher, M. Howells, KTH-dESA/jrc_temba: TEMBA 2.0 (Version v2.0.3). (2021). https://github.com/KTH-dESA/jrc_temba/releases/tag/v2.0.3
O	National Renewable Energy Laboratory, Global CFDDA-based Onshore and Offshore Wind Potential Supply Curves by Country, Class, and Depth (quantities in GW and PWh). (2014). https://openei.org/doe-opendata/dataset/c186913f-6684-4455-a2f2-f26e152a9b35/resource/4dc4a6fd-3a63-47df-bcbe-e9c83b83b38e/download/nrelcfddawindsc20130603.xlsx
P	IRENA, Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), Abu Dhabi.(2019).https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf
Q	I. Pappis, M. Howells, V. Sridharan, F. Gardumi, E. Ramos, W. Usher, et al., Energy projections for African countries. (2019). https://publications.jrc.ec.europa.eu/repository/handle/JRC118432
R	International Energy Agency, IndexMundi, Electric Power Transmission and Distribution Losses (% of output) - Country Ranking – Asia. (2018). https://www.indexmundi.com/facts/indicators/EG.ELC.LOSS.ZS/rankings/asia
S	Y. Li, Y. Chang Y, Infrastructure Investments for Power Trade and Transmission in ASEAN+2: Costs, Benefits, Long-Term Contracts, and Prioritised Development. (2014). https://www.eria.org/ERIA-DP-2014-21.pdf
T	McKinsey, McKinsey Refinery Reference Desk. (2020). [Accessed 13/03/2021]. https://www.mckinseyenergyinsights.com/resources/refinery-reference-desk/
U	IEA ETSAP, Oil Refineries. (2014). https://iea-etsap.org/E-TechDS/PDF/P04_Oil Ref_KV_Apr2014_GSOK.pdf
V	U.S. EIA, Assumptions to the Annual Energy Outlook 2020: International Energy Module.(2020). https://www.eia.gov/outlooks/aeo/assumptions/pdf/international.pdf
W	Asia-Pacific Economic Cooperation, APEC Energy Demand and Supply Outlook 7th Edition. (2019). https://aperc.or.jp/publications/reports/outlook.php
X	ERIA, Cost Analysis of Biomass Power Generation. (2019). https://www.eria.org/uploads/media/12_RPR_FY2018_09_Chapter_5.pdf
Y	Argus, Argus Biomass Markets Weekly Biomass Market News and Analysis Issue 20-47. (2020). https://www.argusmedia.com/-/media/Files/sample-reports/argus-biomassmarkets.ashx?la=en&hash=872E2C03A0A78FE3F236BBF00E7729E3114326E0
Z	P. Howes, J. Bates, A. Brown, R. Diaz-Chavez, S. Christie, A. Bayley, Global Biomass Markets Final Report. (2018). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/795029/Global_Biomass_Markets_Final_report.pdf
AA	IPCC, Emission Factor Database. [accessed 03/02/2021]. https://www.ipcc-nggip.iges.or.jp/EFDB/main.php
AB	S. Hermann, A. Miketa, N. Fichaux, Estimating the Renewable Energy Potential in Africa, Abu Dhabi. (2014). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2014/IRENA_Africa_Resource_Potential_Aug2014.pdf
AC	IRENA, Analysis of Infrastructure for Renewable Power in Eastern and Southern Africa, Abu Dhabi. (2015). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2015/IRENA_Africa_CEC_infrastructure_2015.pdf
AD	United Nations, World Small Hydropower Development Report 2019. (2019). https://www.unido.org/our-focus-safeguarding-environment-clean-energy-access-productive-use-renewable-energy-focus-areas-small-hydro-power/world-small-hydropower-development-report
AE	V. Veng, B. Suryadi, A. Damar Pranadi, A review of renewable energy development and its policy under nationally determined contributions in ASEAN, Int J Smart Grids Clean Energy. (2019). https://accept.aseanenergy.org/wp-content/uploads/2020/01/A-Review-of-RE-and-NDCs-in-ASEAN.pdf
AF	NREL, Exploring Renewable Energy Opportunities in Select Southeast Asian Countries. (2019). https://www.osti.gov/biblio/1527336-exploring-renewable-energy-opportunities-select-southeast-asian-countries-geospatial-analysis-levelized-cost-energy-utility-scale-wind-solar-photovoltaics
AG	NREL, Solar Resources by Class and Country. (2014). https://openei.org/datasets/dataset/solar-resources-by-class-and-country
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AK	BP, Full report – BP Statistical Review of World Energy 2019. (2019). https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2019-full-report.pdf
AL	International Energy Agency, IEA Sankey Diagram. (2019). [accessed 13/03/2021]. https://www.iea.org/sankey/
AM	OLADE, Energy Outlook of Latin America and the Caribbean 2019. (2019). http://biblioteca.olade.org/opac-tmpl/Documentos/old0446b.pdf
AN	A. Shivakumar, M. Brinkerink, T. Niet, W. Usher, OSeMOSYS/osemosys_global: Development release for CCG (Version v0.2.b0). (2021). https://zenodo.org/record/4624417#.Yd2pQmjP02w
AO	IRENA, Southern African Power Pool: Planning and Prospects for Renewable Power, Abu Dhabi. (2013). https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2013/SAPP.pdf
AP	United Nations Development Programme Asia-Pacific Regional Centre, Achieving Sustainable Energy For All in the Asia-Pacific. (2013). https://www.asia-pacific.undp.org/content/rbap/en/home/library/climate-and-disaster-resilience/APRC-EE-2013-SE4ALL.html
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AS	E. Terpilowski-Gill, Decarbonising the Laotian Energy System, Imperial College London. (2020). http://hdl.handle.net/10044/1/86671
AT	O. Okolo, H. Teng, Analysing Nigeria’s Energy system in light of the UN’s Sustainable Development Goals. (2017)h ttps://www.diva-portal.org/smash/get/diva2:1131269/FULLTEXT01.pdf

1.1 Existing Electricity Supply System

The estimated existing power generation capacity in each selected country in 2018 is detailed in Tables 1-6 below (sources B-E). The methods used to calculate these estimates are described in more detail in Section 2.1. Data on the installation year of each power plant can be found in the country datasets published on Zenodo (see Appendix B).

Table 2: Estimated Installed On-Grid Capacity by Technology Type in Selected Countries in Africa in 2018 (sources B-D). ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Biomass	Oil	Coal	Gas CCGT	Gas SCGT	Geothermal	Utility PV	CSP	Large Hydro (>100MW)	Medium Hydro (10-100MW)	Small Hydro (<10MW)	Onshore Wind	Nuclear
Algeria	-	-	-	35738	143	-	54	25	251	24	-	-	-
Angola	-	351	-	-	420	-	14	-	849	71	-	-	-
Benin	-	20	-	139.1	-	-	-	-	-	-	-	-	-
Botswana	-	-	746	-	-	-	-	-	-	-	-	-	-
Burkina Faso	-	267	-	-	-	-	-	-	-	-	-	-	-
Burundi	-	-	-	-	-	-	-	-	-	49	-	-	-
Cameroon	-	168	-	450	200	-	-	-	550	171	-	-	-
Central African Republic	-	14	-	-	-	-	-	-	-	19	-	-	-
Chad	-	217	-	-	-	-	-	-	-	-	-	-	-
Republic of Congo	-	71.1	-	336	-	-	-	-	120	99	-	-	-
Côte D'Ivoire	-	-	-	716	504	-	-	-	549	50	-	-	-
Djibouti	-	107	-	-	-	-	-	-	-	-	-	-	-
Democratic Republic of Congo	-	13	-	-	25	-	-	-	2533	418	-	-	-
Egypt	-	1146	-	16546	294	-	25	20	2700	150	-	810	-
Equatorial Guinea	-	-	-	-	185	-	-	-	150	-	-	-	-
Eritrea	-	133	-	-	-	-	-	-	-	-	-	-	-
Eswatini	120	-	-	-	-	-	-	-	-	51	-	-	-
Ethiopia	-	-	-	-	-	8.5	23	-	3621	191	-	171	-
Gabon	-	16	-	306.9	-	-	-	-	160	170	-	-	-
Gambia	-	70	-	-	-	-	-	-	-	-	-	-	-
Ghana	-	-	-	1251	-	-	-	-	1598	-	-	-	-
Guinea	-	371	-	-	-	-	-	-	240	178	-	-	--
Guinea Bissau	-	18	-	-	-	-	-	-	-	-	-	-	-
Kenya	90	735	-	-	-	419	24	-	499	249	-	310	-
Lesotho	-	-	-	-	-	-	-	-	-	72	-	-	-
Liberia	-	121	-	-	-	-	-	-	-	60	-	-	-
Libya	-	191	-	6762	2120	-	-	-	-	-	-	-	-
Malawi	10	-	-	-	16	-	-	-	252	92	-	-	-
Mali	-	-	-	-	-	-	-	-	200	106	5	-	-
Mauritania	-	133	-	-	120	-	33	-	-	97	-	30	-
Morocco	-	338	2621	1666	-	-	-	690	1207	453	8	1266	-
Mozambique	-	-	-	-	-	-	-	-	2242	44	-	-	--
Namibia	-	60	120	-	-	-	27.4	-	240	92	-	-	-
Niger	-	124	38	-	-	-	-	-	-	-	-	-	-
Nigeria	-	-	-	5158	3261	-	17	-	2040	-	-	-	-
Rwanda	-	28	-	-	26	-	9	-	-	126	3	-	-
Senegal	25	856	-	-	-	-	11	-	120	-	-	-	-
Sierra Leone	-	76	-	-	-	-	-	-	-	50	-	-	-
Somalia	-	15.6	-	-	-	-	1	-	-	-	-	2	-
South Africa	224	2832	38295		376		1765	450	601	42	7	2032	1830
South Sudan	-	64.9	-	-	-	-	-	-	-	-	-	-	-
Sudan	191	926	-	319	-	-	-	-	2196	56	-	-	-
Tanzania	70	100	-	208	213	-	-	-	384	169	8	-	-
Togo	-	101	-	28	-	-	-	-	-	66	-	-	-
Tunisia	-	-	-	3559	145	-	22	-	-	57.8	8	245	-
Uganda	60	136	-	-	-	-	20	-	635	71.5	-	-	-
Zambia	39.8	170	300	-	-	-	-	-	2308	12	-	-	-
Zimbabwe	97	9.83	1117.5	-	-	-	-	-	680	-	-	-	-

Table 3: Estimated Installed On-Grid Capacity by Technology Type in Selected Countries in East Asia in 2018 (sources B-D) ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Biomass	Oil	Coal	Gas CCGT	Gas SCGT	Geothermal	Utility PV	Large Hydro (>100MW)	Medium Hydro (10-100MW)	Small Hydro (<10MW)	Onshore Wind	Nuclear
Cambodia	2	54	506	-	-	-	12	897	33	-	-	-
Indonesia	1740	751.7	34397	13799	1170	1404	-	4539.41	505	42	-	-
Laos	30	-	1876	-	-	-	-	4221.14	222	19	-	-
Malaysia	1040	178	14334	14324	626	-	262	4500	168	-	-	-
Myanmar	-	0	252.49	1448.6	35	-	12	2568.8	647	-	-	-
Papua New Guinea	-	361.9	-	-	186.2	56	-	-	271	4	-	-
Philippines	190	2709	10699	4237.5	100	1918.8	818	3393.9	208	1	220.1	-
South Korea	858	3255	38260	33583	118	-	3533	4827	374	62	768.9	23080
Taiwan	740	2632	18650	13561	-	-	842	3778	41	3	646.64	5216
Thailand	3230		5259	27132	-	-	1415	3639	148	-	210	-
Vietnam	255	970.7	14935	8201.5	-	-	-	13534	2957	266	287	-

Table 4: Estimated Installed On-Grid Capacity by Technology Type in Selected Countries in South America in 2018 (sources B-D) ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Biomass	Oil	Coal	Gas CCGT	Gas SCGT	Utility PV	Large Hydro (>100MW)	Medium Hydro (10-100MW)	Small Hydro (<10MW)	Onshore Wind	Nuclear
Argentina	660	1755.55	2634.54	13775.38	1362	3	9204	589	52	279.32	1764
Bolivia	150	-	-	1315.58	383	13	-	496.9	-	-	-
Brazil	12271.5	13403.2	4145.5	19287.36	718.6	6	88787	6917	1062	11378.2	1990
Chile	466.1	3365.18	4835	4878.89	72	618.15	5698	1460	165	909.18	-
Colombia	240	188	775.9	3151.06	-	-	11434	66	-	20	-
Ecuador	140	1693.3	-	146	1420.54	30	3089	167	-	20	-
Paraguay	40	-	-	-	-	-	8764	50	-	-	-
Peru	180	-	230.67	7373.13	75	96	4105.6	46	-	150	-
Uruguay	425.3	905.2	-	570.34	54	227	1538	-	-	1384	-
Venezuela	-	3570	-	10857	680	-	17560	105	-	30	-

Table 5: Estimated Installed Off-Grid PV and Hydropower in Selected Countries in Africa in 2018 (source E) ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Off-Grid PV	Off-Grid Hydropower
Algeria	423	-
Angola	13.38	6.86
Benin	-	-
Botswana	1.61	-
Burkina Faso	27	-
Burundi	4.7	1.6
Cameroon	14.19	0.3
Central African Republic	0.3	0.2
Chad	0.17	-
Republic of Congo	0.57	-
Côte D'Ivoire	8.28	-
Djibouti	0.36	-
Democratic Republic of Congo	18.9	131.7
Egypt	50	-
Equatorial Guinea	-	-
Eritrea	10.16	-
Eswatini	0.8	1.7
Ethiopia	13.94	1.28
Gabon	1.4	0.29
Gambia	2	-
Ghana	7.59	-
Guinea	13.28	2.22
Guinea Bissau	1.17	-
Kenya	37.84	6.44
Lesotho	0.16	0.18
Liberia	2.58	4
Libya	5.11	0
Malawi	21.88	1.7
Mali	19.58	-
Mauritania	21.07	-
Morocco	22.9	-
Mozambique	15	1.06
Namibia	21.93	-
Niger	20.04	-
Nigeria	17.57	0.4
Rwanda	25.4	-
Senegal	12	-
Sierra Leone	4.25	4.89
Somalia	7.07	-
South Africa	-	7.19
South Sudan	0.55	-
Sudan	12.58	-
Tanzania	25.34	15.42
Togo	3	-
Tunisia	2.08	-
Uganda	28	5.4
Zambia	0.22	2.61
Zimbabwe	7.91	1.6

Table 6: Estimated Installed Off-Grid PV and Hydropower in Selected Countries in East Asia in 2018 (source E). ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Off-Grid PV	Off-Grid Hydropower
Cambodia	1.94	-
Indonesia	45.08	14.85
Laos	1.63	2.01
Malaysia	7.44	0.45
Myanmar	47.54	6.89
Papua New Guinea	1.23	76.4
Philippines	1.16	20.59
South Korea	-	-
Taiwan	-	-
Thailand	-	-
Vietnam	5.26	43

Table 7: Estimated Installed Off-Grid PV and Hydropower in Selected Countries in South America in 2018 (source E). ‘-’ denotes 0 estimated capacity.

Country	Estimated Installed Capacity (MW)
Country	Off-Grid PV	Off-Grid Hydropower
Argentina	0.62	19.58
Bolivia	5.54	9.2
Brazil	7.23	0.02
Chile	-	-
Colombia	1.53	5.32
Ecuador	2.02	26
Paraguay	0.06	-
Peru	53.58	177.65
Uruguay	2.27	-
Venezuela	3.4	0.81

1.2 Techno-economic Data for Electricity Generation Technologies

The techno-economic parameters of electricity generation technologies by region are presented in Tables 7-9, including costs, operational lives, efficiencies and average capacity factors. For countries in Africa, cost (capital and fixed), operational life and efficiency data were collected from reports by the International Renewable Energy Agency (sources F-H) and applied to all of Africa. These cost data include projected cost reductions for renewable energy technologies, which are presented in Table 11. Cost (capital and fixed), operational life and efficiency data for countries in East Asia are based on reports by the International Renewable Energy Agency (IRENA) and the ASEAN Centre for Clean Energy (ACE) (sources I-J). Cost (capital and fixed), operational life and efficiency data for countries in South America are based on the data used in the South America Model Base (SAMBA) (source K). Where technologies were not included in SAMBA, namely diesel generation technologies, medium hydropower plants and decentralised solar PV with storage, costs were estimated based on costs in other regions. For countries in Asia and South America, projected cost reductions for renewable energy technologies were estimated by applying the cost reduction trends of IRENA for Africa (source F), published in 2021, to current Asia- and South America-specific cost estimates. The cost and performance of parameters of fossil electricity generation technologies are assumed constant over the modelling period. Only fixed power plant costs are considered in this analysis, which captures variable operation and maintenance costs. Country-specific capacity factors for solar PV, onshore wind and hydropower technologies for every country were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset (sources B, L, M). Country-specific capacity factors for offshore wind were sourced from the TEMBA dataset (source N) for countries in Africa and an NREL dataset for countries in East Asia and South America (source O). Regional capacity factor estimates for other technologies were sourced from the IRENA (sources G, J) for Africa, SAMBA for South America (source K), and IRENA and ACE for Asia (source I). Average capacity factors were calculated for each technology and presented below, with daytime (6 am - 6 pm) averages presented for solar PV technologies. For more information on the capacity factor data, refer to Section 2.1.

Table 8: Techno-economic parameters of electricity generation technologies in Africa (sources F, G, P)

Technology	Capital Cost ($/kW in 2020)	Fixed Cost ($/kW/yr in 2020)	Operational Life (years)	Efficiency	Average Capacity Factor
Biomass Power Plant	2500	75	30	35%	50%
Coal Power Plant	2500	78	35	37%	85%
Geothermal Power Plant	4000	120	25	80%	79%
Light Fuel Oil Power Plant	1200	35	25	35%	80%
Oil Fired Gas Turbine (SCGT)	1450	45	25	35%	80%
Gas Power Plant (CCGT)	1200	35	30	48%	85%
Gas Power Plant (SCGT)	700	20	25	30%	85%
Solar PV (Utility)	1378	18	24	100%	Varies by country
CSP without Storage	4058	41	30	100%	23%
CSP with Storage	5797	58	30	100%	26%
Large Hydropower Plant (Dam) (>100MW)	3000	90	50	100%	Varies by country
Medium Hydropower Plant (10-100MW)	2500	75	50	100%	Varies by country
Small Hydropower Plant (<10MW)	3000	90	50	100%	Varies by country
Onshore Wind	1489	60	25	100%	Varies by country
Offshore Wind	3972	159	25	100%	Varies by country
Nuclear Power Plant	6137	184	50	33%	85%
Light Fuel Oil Standalone Generator (1kW)	750	23	10	16%	30%
Solar PV (Distributed with Storage)	4320	86	24	100%	Varies by country

Table 9: Techno-economic parameters of electricity generation technologies in East Asia (sources I-J)

Technology	Capital Cost ($/kW in 2020)	Fixed Cost ($/kW/yr in 2020)	Operational Life (years)	Efficiency	Average Capacity Factor
Biomass Power Plant	2750.0	69.0	25	0.38	0.7
Coal Power Plant	1300.0	52.0	60	0.3	0.75
Geothermal Power Plant	2500.0	100.0	50	0.1	0.7
Light Fuel Oil Power Plant	1200.0	18.0	50	0.4	0.25
Oil Fired Gas Turbine (SCGT)	1344.0	18.0	50	0.4	0.25
Gas Power Plant (CCGT)	1000.0	40.0	30	0.55	0.55
Gas Power Plant (SCGT)	784.0	23.0	30	0.35	0.55
Solar PV (Utility)	1160.0	15.08	30	1.0	Varies by country
CSP with Storage	4965.31	120.0	35	0.33	0.3
Large Hydropower Plant (Dam) (>100MW)	1539.0	46.17	40	1.0	Varies by country
Medium Hydropower Plant (10-100MW)	1592.86	47.79	40	1.0	Varies by country
Small Hydropower Plant (<10MW)	2162.0	64.86	40	1.0	Varies by country
Onshore Wind	2220.09	88.8	30	1.0	Varies by country
Offshore Wind	2876.21	115.05	30	1.0	Varies by country
Nuclear Power Plant	5500.0	138.0	60	0.33	0.83
Light Fuel Oil Standalone Generator (1kW)	1500.0	38.0	20	0.42	0.4
Solar PV (Distributed with Storage)	2130.8	42.62	24	1.0	Varies by country

Table 10: Techno-economic parameters of electricity generation technologies in South America (sources J-K)

Technology	Capital Cost ($/kW in 2020)	Fixed Cost ($/kW/yr in 2020)	Operational Life (years)	Efficiency	Average Capacity Factor
Biomass Power Plant	1905.0	13.0	25	0.35	0.7
Coal Power Plant	2500.0	40.0	40	0.43	0.75
Geothermal Power Plant	3796.47	100.0	20	0.11	0.7
Light Fuel Oil Power Plant	1200.0	15.0	25	0.35	0.25
Oil Fired Gas Turbine (SCGT)	1400.0	25.0	25	0.35	0.25
Gas Power Plant (CCGT)	1260.0	20.0	30	0.57	0.55
Gas Power Plant (SCGT)	583.0	10.0	30	0.38	0.55
Solar PV (Utility)	1524.5	19.8	25	1.0	Varies by country
CSP with Storage	5797.0	57.97	40	0.35	0.3
Large Hydropower Plant (Dam) (>100MW)	2939.0	88.17	60	1.0	Varies by country
Medium Hydropower Plant (10-100MW)	2500.0	75.0	60	1.0	Varies by country
Small Hydropower Plant (<10MW)	3499.0	104.9	60	1.0	Varies by country
Onshore Wind	1375.6	55.0	30	1.0	Varies by country
Offshore Wind	3406.3	136.2	25	1.0	Varies by country
Nuclear Power Plant	6318.0	189.54	40	0.35	0.83
Light Fuel Oil Standalone Generator (1kW)	750.0	23.0	20	0.42	0.4
Solar PV (Distributed with Storage)	4320.0	86.4	24	1.0	Varies by country

Table 11: Projected costs of renewable energy technologies for selected years to 2050 in Africa. (sources F, P)

Renewable Energy Technology	Capital Cost ($/kW)
Renewable Energy Technology	2015	2020	2025	2030	2040	2050
Biomass Power Plant	2500	2500	2500	2500	2500	2500
Geothermal Power Plant	4000	4000	4000	4000	4000	4000
Solar PV (Utility)	2165	1378	984	886	723	723
CSP without Storage	6051	4058	3269	2634	2562	2562
CSP with Storage	8645	5797	4670	3763	3660	3660
Large Hydropower Plant (Dam) (>100MW)	3000	3000	3000	3000	3000	3000
Medium Hydropower Plant (10-100MW)	2500	2500	2500	2500	2500	2500
Small Hydropower Plant (<10MW)	3000	3000	3000	3000	3000	3000
Onshore Wind	1985	1489	1191	1087	933	933
Offshore Wind	5000	3972	3021	2450	2275	2100
Solar PV (Distributed with Storage)	6840	4320	3415	2700	2091	2091

Table 12: Projected costs of renewable energy technologies in East Asia for selected years to 2050. (sources F, I, J, P)

Renewable Energy Technology	Capital Cost ($/kW)
Renewable Energy Technology	2015	2020	2025	2030	2040	2050
Biomass Power Plant	2750.0	2750.0	2750.0	2750.0	2750.0	2750.0
Solar PV (Utility)	1822.5	1160.0	828.33	745.83	608.62	608.62
CSP with Storage	7404.71	4965.31	4000.0	3223.13	3134.9	3134.9
Large Hydropower Plant (Dam) (>100MW)	1539.0	1539.0	1539.0	1539.0	1539.0	1539.0
Medium Hydropower Plant (10-100MW)	1592.86	1592.86	1592.86	1592.86	1592.86	1592.86
Small Hydropower Plant (<10MW)	2162.0	2162.0	2162.0	2162.0	2162.0	2162.0
Onshore Wind	2959.63	2220.09	1775.78	1620.71	1391.1	1391.1
Offshore Wind	3620.25	2876.21	2187.28	1773.92	1647.21	1520.5
Solar PV (Distributed with Storage)	3502.0	2130.8	1880.8	1755.8	1690.8	1625.8

Table 13: Projected costs of renewable energy technologies in South America for selected years to 2050. (sources F, K, P)

Renewable Energy Technology	Capital Cost ($/kW)
Renewable Energy Technology	2015	2020	2025	2030	2040	2050
Biomass Power Plant	1905.0	1905.0	1905.0	1905.0	1905.0	1905.0
Solar PV (Utility)	1898.79	1791.02	1683.26	1575.49	1359.96	1144.43
CSP with Storage	8652.93	5797.0	4670.0	3763.0	3660.0	3660.0
Large Hydropower Plant (Dam) (>100MW)	2939.0	2939.0	2939.0	2939.0	2939.0	2939.0
Medium Hydropower Plant (10-100MW)	2500.0	2500.0	2500.0	2500.0	2500.0	2500.0
Small Hydropower Plant (<10MW)	3499.0	3499.0	3499.0	3499.0	3499.0	3499.0
Onshore Wind	1620.0	1582.33	1544.65	1506.98	1431.63	1356.28
Offshore Wind	4104.0	3928.19	3752.37	3576.56	3224.93	2873.3
Solar PV (Distributed with Storage)	6840.0	4320.0	3415.0	2700.0	2091.0	2091.0

Table 14: Estimated Average Capacity Factors in Selected Countries in Africa (sources B, C, L, M, Q)

Country	Hydropower	Solar PV	Onshore Wind	Offshore Wind
Algeria	0.11	0.35	0.21	0.37
Angola	0.52	0.32	0.11	0.12
Benin	0.36	0.27	0.13	0.13
Botswana	0.23	0.35	0.21	n/a
Burkina Faso	0.35	0.37	0.17	n/a
Burundi	0.42	0.28	0.06	n/a
Cameroon	0.6	0.31	0.07	0.1
Central African Republic	0.7	0.28	0.08	n/a
Chad	0.43	0.33	0.26	n/a
Republic of Congo	0.47	0.25	0.03	0.09
Côte D'Ivoire	0.33	0.17	0.09	0.1
Djibouti	0.41	0.29	0.21	0.36
Democratic Republic of Congo	0.34	0.26	0.06	n/a
Egypt	0.54	0.36	0.22	0.4
Equatorial Guinea	0.19	0.23	0.03	0.08
Eritrea	0.41	0.3	0.16	0.46
Eswatini	0.42	0.32	0.14	n/a
Ethiopia	0.41	0.37	0.18	0.48
Gabon	0.55	0.25	0.04	0.1
Gambia	0.41	0.28	0.14	0.14
Ghana	0.58	0.27	0.1	0.1
Guinea	0.43	0.35	0.08	0.11
Guinea Bissau	0.41	0.26	0.11	0.15
Kenya	0.48	0.32	0.21	0.45
Lesotho	0.69	0.36	0.15	n/a
Liberia	0.6	0.27	0.07	0.37
Libya	0.41	0.35	0.29	0.4
Malawi	0.54	0.44	0.16	n/a
Mali	0.54	0.37	0.22	n/a
Mauritania	0.41	0.37	0.35	0.41
Morocco	0.13	0.37	0.36	0.33
Mozambique	0.68	0.3	0.18	0.3
Namibia	0.59	0.41	0.15	n/a
Niger	0.36	0.37	0.27	n/a
Nigeria	0.36	0.34	0.15	0.37
Rwanda	0.38	0.33	0.06	n/a
Senegal	0.41	0.41	0.17	0.37
Sierra Leone	0.36	0.26	0.08	0.37
Somalia	0.41	0.29	0.52	0.58
South Africa	0.23	0.38	0.25	0.36
South Sudan	0.5	0.19	0.15	n/a
Sudan	0.43	0.26	0.22	0.28
Tanzania	0.47	0.38	0.14	0.3
Togo	0.23	0.26	0.12	0.37
Tunisia	0.13	0.33	0.21	0.41
Uganda	0.54	0.31	0.08	n/a
Zambia	0.63	0.32	0.21	n/a
Zimbabwe	0.68	0.34	0.2	n/a

Table 15: Estimated Average Capacity Factors in Selected Countries in East Asia (sources B, C, L, M, O)

Country	Hydropower	Solar PV	Onshore Wind	Offshore Wind
Cambodia	0.31	0.33	0.09	0.19
Indonesia	0.32	0.4	0.03	0.2
Laos	0.55	0.28	0.08	n/a
Malaysia	0.35	0.27	0.04	0.18
Myanmar	0.45	0.34	0.09	0.21
Papua New Guinea	0.38	0.27	0.11	0.24
Philippines	0.26	0.19	0.2	0.23
South Korea	0.2	0.25	0.19	0.24
Taiwan	0.19	0.24	0.27	0.34
Thailand	0.25	0.38	0.16	0.19
Vietnam	0.49	0.23	0.15	0.27

Table 16: Estimated Average Capacity Factors in Selected Countries in South America (sources B, C, L, M, O)

Country	Hydropower	Solar PV	Onshore Wind	Offshore Wind
Argentina	0.42	0.38	0.32	0.45
Bolivia	0.54	0.2	0.13	n/a
Brazil	0.62	0.29	0.33	0.24
Chile	0.49	0.5	0.28	0.4
Colombia	0.5	0.24	0.41	0.27
Ecuador	0.52	0.3	0.07	0.18
Paraguay	0.72	0.29	0.2	n/a
Peru	0.66	0.44	0.29	n/a
Uruguay	0.52	0.17	0.27	0.31
Venezuela	0.63	0.28	0.2	0.3

1.3 Techno-economic Data for Power Transmission and Distribution

The techno-economic parameters of transmission and distribution technologies are taken from The Reference Case scenario of The Electricity Model Base for Africa (TEMBA) (source Q) for countries in Africa. This gives estimated transmission and distribution efficiencies projected to 2050 and estimated costs and operational lives. For countries in Asia, combined losses in electricity transmission and distribution are estimated based on an International Energy Agency (IEA) dataset presented by Index Mundi (source R), which gives estimated combined losses in 2014. It was then assumed that combined losses would be reduced to 5% by 2050, falling linearly. The combined costs of power transmission and distribution are estimated based on a report by the Economic Research Institute for ASEAN and East Asia (ERIA) (source S), which gives cost estimates for several real-life projects in ASEAN. For countries in South America, the efficiencies and costs of power transmission and distribution were taken from the SAMBA dataset (source K), which gives estimated efficiencies by country, including projections to 2063. The estimated combined efficiencies of transmission and distribution in each included country are presented in the following tables.

Table 17: Estimated combined efficiency of transmission and distribution in selected countries in Africa in 2020, 2030 & 2050 (source Q)

Country	Estimated combined efficiency of transmission & distribution
Country	2020	2030	2050
Algeria	71.3%	74.1%	77.9%
Angola	89.3%	90.3%	90.3%
Benin	77.9%	78.9%	80.8%
Botswana	81.6%	82.6%	85.4%
Burkina Faso	49.4%	51.3%	55.1%
Burundi	86.5%	87.4%	89.3%
Cameroon	77.0%	77.9%	79.8%
Central African Republic	77.0%	77.9%	79.8%
Chad	77.0%	77.9%	79.8%
Republic of Congo	52.3%	54.2%	58.0%
Côte D'Ivoire	80.8%	81.7%	81.7%
Djibouti	77.9%	78.9%	80.8%
Democratic Republic of Congo	87.5%	87.4%	89.3%
Egypt	87.4%	88.3%	90.2%
Equatorial Guinea	74.1%	76.0%	79.8%
Eritrea	80.8%	81.7%	83.6%
Eswatini	90.3%	90.3%	90.3%
Ethiopia	87.4%	88.3%	90.2%
Gabon	58.9%	60.8%	64.6%
Gambia	77.9%	79.8%	81.7%
Ghana	77.0%	77.9%	79.8%
Guinea	90.3%	90.3%	92.2%
Guinea Bissau	43.7%	45.6%	50.4%
Kenya	81.7%	83.6%	88.4%
Lesotho	83.6%	88.4%	89.3%
Liberia	71.3%	74.1%	77.9%
Libya	67.5%	69.4%	73.2%
Malawi	78.9%	79.8%	81.7%
Mali	79.8%	79.8%	81.7%
Mauritania	59.9%	61.8%	65.6%
Morocco	86.5%	87.4%	89.3%
Mozambique	82.7%	84.6%	89.3%
Namibia	90.2%	91.2%	93.1%
Niger	74.1%	76.0%	79.8%
Nigeria	86.5%	87.4%	89.3%
Rwanda	66.5%	75.1%	78.9%
Senegal	86.5%	87.4%	89.3%
Sierra Leone	54.2%	56.1%	63.7%
Somalia	53.2%	55.1%	62.7%
South Africa	91.2%	92.2%	92.2%
South Sudan	90.3%	90.3%	92.2%
Sudan	90.3%	90.3%	91.2%
Tanzania	83.6%	84.6%	87.4%
Togo	86.5%	87.4%	89.3%
Tunisia	83.6%	84.6%	87.4%
Uganda	82.7%	83.6%	86.5%
Zambia	86.4%	87.4%	89.3%

Table 18: Estimated combined efficiency of transmission and distribution in selected countries in Asia in 2020, 2030 & 2050 (source R)

Country	Estimated combined efficiency of transmission & distribution
Country	2020	2030	2050
Cambodia	80.0%	85.0%	95.0%
Indonesia	91.0%	93.0%	95.0%
Laos	91.0%	92.0%	95.0%
Malaysia	94.0%	95.0%	95.0%
Myanmar	82.0%	86.0%	95.0%
Papua New Guinea	90.0%	92.0%	95.0%
Philippines	91.0%	93.0%	95.0%
South Korea	86.0%	89.0%	95.0%
Taiwan	92.0%	93.0%	95.0%
Thailand	94.0%	94.3%	95.0%
Vietnam	91.0%	93.0%	95.0%

Table 19: Estimated combined efficiency of transmission and distribution in selected countries in South America in 2020, 2030 & 2050 (source K)

Country	The estimated combined efficiency of transmission & distribution
Country	2020	2030	2050
Argentina	87.4%	89.3%	90.2%

Selected ‘Starter kit’ energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

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Abstract

Figures

Value Of The Data

1. Data Description