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

The data provided in this paper can be used as input data to develop an energy system model for Paraguay. 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 datales are available as Supplementary Materials. Appendix gure A3 for Paraguay is repeated below. This is purely illustrative. It shows a zero-order model of the production of electricity by technology over the period 2020 to 2050 for a least cost energy future. 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, emissions factors and nal electricity demands. Below shows the different items and their description, in order of appearance, presented in this article. Abstract 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 energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Paraguay, 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 three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) 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.

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 three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) 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.
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].

Item
Description of Content

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 are based on the data used in the South America Model Base [7] and are applicable to South America. Projected cost reductions for renewable energy technologies were estimated by applying the cost reduction trends from a 2021 IRENA report focussing on Africa [8] to these South America-speci c current cost estimates. These projections are presented in Table 3 [3,7,8,9,10,11,12]

Techno-economic Data for Power Transmission and Distribution
The e ciency of power transmission and distribution were taken from the SAMBA dataset [7], which gives estimated e ciencies by country, including projected e ciencies to 2063. The e ciencies of transmission and distribution in Paraguay are therefore assumed to reach 96.0% and 83.0% in 2030 and 96.0% and 91.0% in 2050 respectively. The costs and operational life of transmission and distribution technologies were also taken from SAMBA, which gives estimates relevant to South America, including future projections. Paraguay has an estimated 8kb/d domestic re nery capacity [13]. 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 estimates based on an international oil price forecast [15] for oil and oil products, the SAMBA dataset [7] for natural gas, and a report on international biomass markets [16]. More detail 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 Paraguay. Geothermal MW 0 Table 9: Estimated Fossil Fuel Reserves [7,21] Proven Reserves

Renewable and Fossil Fuel Reserves
Coal (million tonnes) 0 Crude Oil (billion barrels) 0 Natural Gas (trillion cubic feet) 0

Electricity Demand Projection
An electricity demand projection was calculated based on the Current Policy Scenario regional demand projections of the OLADE Energy Outlook 2019 [22], which were divided by country based on historic consumption data from the International Energy Agency ( [7]. The data sources used are detailed in this section.

Electricity Supply System Data
Data on Paraguay's existing on-grid power generation capacity, presented in Table 1, were extracted from the PLEXOS World dataset [3,4,5] using scripts from OSeMOSYS global model generator [24]. PLEXOS World provides estimated capacities and commissioning dates by power plant, based on the World Resources Institute Global Power Plant database [5].These data were used to estimate installed capacity in future years based on the operational life data in Table 2. Data on Paraguay's off-grid renewable energy capacity were sourced from yearly capacity statistics produced by IRENA [6]. Cost, e ciency and operational life data in Table 2 were primarily collected from the SAMBA dataset [7], which provides estimates for these parameters by technology in South America. Where estimates were not available in SAMBA, costs were extrapolated from reports by IRENA for diesel electricity generation, medium hydropower, and off-grid solar PV [8,9]. The costs of renewable energy technologies are expected to fall in the future. In order to calculate estimated cost reductions in the region, technology-speci c cost reduction trends from a very recent IRENA report focussing on Africa [8] were applied to the regional current cost estimates used from SAMBA [7,8,9]. For offshore wind, the cost reduction trend was instead taken from a technology-speci c IRENA report on the future of wind [25] since it is not featured in [8]. The resulting cost projections are presented in Table 3 and Fig. 2. It is assumed that costs fall linearly between data points and those costs remain constant beyond 2040 when the IRENA forecasts end (except for offshore wind, where the IRENA forecast continues to 2050). An illustrative example of a zero-order least-cost energy scenario for Paraguay's electricity production produced using the data presented in this paper.

Figure 2
Projected costs of renewable energy technologies for selected years to 2050 [7,8,9]   Final Electricity Demand Projection (PJ) [22,23] Supplementary Files This is a list of supplementary les associated with this preprint. Click to download. ParaguayFF.txt ParaguayNZv2.txt ParaguayLCv2.txt Appendix.docx