Selected ‘Starter Kit’ energy system modelling data for South Sudan (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for South Sudan, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and 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 Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020-2050. The assumptions used and 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. Table   Subject Energy Value of the data These data can be used to develop national energy system models to inform national energy investment outlooks and policy plans, as well as provide insights on the evolution of the electricity supply system under different trajectories.

Speci cations
The data are useful for country analysts, policy makers and the broader scienti c community, as a zero-order starting point for model development.
These data could be used to examine a range of possible energy system pathways, in addition to the examples given in this study, to provide further insights on the evolution of the country's power system.
The data can be used both for conducting an analysis of the power system but also for capacity building activities. Also, the methodology of translating the input data into modelling assumptions for a cost-optimization tool is presented here which is useful for developing a zero order Tier 2 national energy model [1]. This is consistent with U4RIA energy planning goals [2].

Data Description
The data provided in this paper can be used as input data to develop an energy system model for South Sudan. As an illustration, these data were used to develop an energy system model using the cost-optimization tool OSeMOSYS for the period 2015-2050. For reference, that model is described in Appendix A and its data les are available as Supplementary Materials. Figure 1 shows a zero-order model of the production of electricity by technology for South Sudan over the period 2020 to 2050 for a least cost energy future, repeated from the appendix. This is purely illustrative. Using the data described in this article, the analyst can reproduce this, as well as many other scenarios, such as net-zero by 2050, in a variety of energy planning toolkits.
The data provided were collected from publicly available sources, including the reports of international organizations, journal articles and existing model databases. The dataset includes the techno-economic parameters of supply-side technologies, installed capacities, emission factors and nal electricity demands. Below shows the different items and their description, in order of appearance, presented in this article.

Item
Description of Content

Existing Electricity Supply System
The total power generation capacity in South Sudan is estimated at 65.44 MW in 2018 [3,4,5]. The estimated existing power generation capacity is detailed in Table 1 below [3,4,5]. The methods used to calculate these estimates are described in more detail in Sect. 2.1. Data on the installation year of each power plant can be found in the country dataset published on Zenodo.

Techno-economic Data for Electricity Generation Technologies
The techno economic parameters of electricity generation technologies are presented in Table 2, including costs, operational lives, e ciencies and average capacity factors. Cost (capital and xed), operational life and e ciency data were collected from reports by the International Renewable Energy Agency [6, 7,8] and are applicable to all of Africa. These cost data include projected cost reductions for renewable energy technologies, which are presented in Table 3. The cost and performance of parameters of fossil electricity generation technologies are assumed constant over the modelling period. In this analysis only xed power plant costs are considered, which capture variable operation and maintenance costs. Country-speci c capacity factors for solar PV, wind and hydropower technologies in South Sudan were sourced from the TEMBA dataset [3]. Capacity factors for other technologies were sourced from the International Renewable Energy Agency [7,9] and are applicable to all of Africa. Average capacity factors were calculated for each technology and presented in the table below, with daytime (6am − 6pm) averages presented for solar PV technologies. For more information on the capacity factor data, refer to Sect. 2.1.

Techno-economic Data for Power Transmission and Distribution
The techno-economic parameters of transmission and distribution technologies were taken from the Reference Case scenario of The Electricity Model Base for Africa (TEMBA) [10]. According to these data, the e ciencies of power transmission and distribution in South Sudan are assumed to reach 95.0% and 95.0% respectively in 2030. In the following table, the technoeconomic parameters associated with the transmission and distribution network are presented.  [11]. In the OSeMOSYS model, two oil re nery technologies were made available for investment in the future, each with different output activity ratios for Heavy Fuel Oil (HFO) and Light Fuel Oil (LFO).
The technoeconomic data for these technologies are shown in Table 5.

Fuel Prices
Assumed costs are provided for both imported and domestically-extracted fuels. The fuel price projections until 2050 are presented below. These are generic estimates based on an international oil price forecast [13] and cost estimates for Africa [7]. A detailed explanation of how these estimates were calculated is provided in Sect. 2.2.

Emission Factors
Fossil fuel technologies emit several greenhouse gases, including carbon dioxide, methane and nitrous oxides throughout their operational lifetime. In this analysis, only carbon dioxide emissions are considered. These are accounted for using carbon dioxide emission factors assigned to each fuel, rather than each power generation technology. The assumed emission factors are presented in Table 7.  Tables 8 and 9 show estimated domestic renewable energy potentials and fossil fuel reserves respectively in South Sudan.   [3] in a reference scenario. Figure 4 shows the nal electricity demand projection.

Electricity Supply System Data
Data on South Sudan's existing on-grid power generation capacity, presented in Table 1, were extracted from the TEMBA dataset [3], which provides estimates residual capacity by power plant type from 2015. The 33MW Juba oil-red power plant, commissioned in 2019, was also included [4]. Data on South Sudan's off-grid renewable energy capacity were sourced from yearly capacity statistics produced by IRENA [5]. Cost, e ciency and operational life data in Table 2 were collected from reports by IRENA [6,7,8], which provide generic estimates for these parameters by technology. These reports also provide projections of future costs for renewable energy technologies. These data are presented in Table 3 and Figure 2, where it was assumed that costs fall linearly between the data points provided by IRENA and that costs remain constant beyond 2040 when the IRENA forecasts end.
Country-speci c capacity factors for solar PV, wind and hydropower were sourced from the TEMBA dataset [3], which provides estimated capacity factors by country for 8 time slices, the average values of which are presented in Table 2. These data were also used to estimate capacity factors for 8 time slices used in the OSeMOSYS model (see detail in Annex 1). Capacity factors for other technologies were sourced from reports by IRENA [7,9], which provide generic estimates for each technology. The costs and e ciencies of power transmission and distribution were sourced from TEMBA reference case [3], which provides generic cost estimates and country-speci c e ciencies which consider expected e ciency improvements in the future. Technoeconomic data for re neries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) [12], which provides generic estimates of costs and performance parameters, while the re nery options modelled are based on the methods used in TEMBA [10].

Fuel Data
The crude oil price is based on an international price forecast produced by the US Energy Information Administration (EIA), which runs to 2050 [13]. The price was increased by 10% for imported oil to re ect the cost of importation. The price of imported HFO and LFO were calculated by multiplying the oil price by 0.8 and 1.33 respectively, based on the methods used in TEMBA [10]. The prices of coal, natural gas and biomass were sourced from an IRENA report [7], which provides generic estimates for costs to 2030. Again, a linear rate of change was assumed between data points from IRENA, and the forecast was extended to 2040 using the rate of change between 2020 and 2030. Prices were then assumed constant after 2040. The cost of domestically-produced biomass was increased by 10% to estimate a cost of imported biomass.

Emissions Factors and Domestic Reserves
Emissions factors were collected from the IPCC Emission Factor Database [14], which provides carbon emissions factors by fuel. Domestic renewable energy potentials for solar PV, CSP and wind were collected from an IRENA-KTH working paper [15], which provides estimates of potential yearly generation by country in Africa. Based on country area proportions, the potentials given for Sudan in the IRENA-KTH report were divided between Sudan and South Sudan, with approximately one third of the potentials assigned to South Sudan and presented in this article.

Electricity Demand Data
The nal electricity demand projection is based on data from the TEMBA Reference Scenario dataset [3], which provides yearly total demand estimates from 2015-2070 under a reference case scenario.  Figure 1 An illustrative example of a zero-order least-cost energy scenario for South Sudan produced using the data presented in this paper. Fuel price projections to 2050 [13,7] Page 15/15

Figure 4
Final Electricity Demand Projection (PJ) [3] Supplementary Files This is a list of supplementary les associated with this preprint. Click to download. Appendix.docx SouthSudanLeastCost.txt SouthSudanFossilFuture.txt