Selected ‘Starter Kit’ energy system modelling data for Myanmar (#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 energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Myanmar, 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.

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].

Data Description
The data provided in this paper can be used as input data to develop an energy system model for Myanmar. 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 over the period 2020 to 2050 for a least cost energy future. 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, emissions factors and nal electricity demands. Below shows the different items and their description, in order of appearance, presented in this article.

Existing Electricity Supply System
The total power generation capacity in Myanmar is estimated at 5018.33 MW in 2018 [3,4,5,6]. The estimated existing power generation capacity is detailed in Table 1 below [3,4,5,6]. The methods used to calculate these estimates are described in more detail in Section 2.1.

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 reports by the International Renewable Energy Agency (IRENA) and the ASEAN Centre for Clean Energy (ACE) [7,8] and are applicable to Asia.
Projected cost reductions for renewable energy technologies were estimated by applying the cost reduction trends from a 2021 IRENA report focussing on Africa [9] to these Asia-speci c current cost estimates. These projections are presented in Table 3. The cost and performance of parameters of fossil electricity generation technologies are assumed constant over the modelling period. Countryspeci c capacity factors for solar PV, wind and hydropower technologies in Myanmar were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset [3,10,11], as well as an NREL dataset [12]. Capacity factors for other technologies were sourced from IRENA and ACE [7] and are applicable to Asia. 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 Section 2.1.   [14], which gives cost estimates for several real-life projects in ASEAN. For more detail, see section 2.In the following table, the techno-economic parameters associated with the transmission and distribution network are presented. Myanmar has an estimated 57kb/d domestic re nery capacity [15]. 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 Asia-speci c cost estimates produced by the Asia Paci c Economic Cooperation (APEC) and ERIA [17.18], with an international average biomass price in 2020 assumed for imported biomass [19]. More detail is provided in Section 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 Myanmar.

Electricity Supply System Data
Data on Myanmar'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 [26]. 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 Myanmar'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 collected from reports by IRENA and ACE [7,8], which provide estimates for these parameters by technology in ASEAN and other Asian countries. 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 [9] were applied to the current Asia-speci c cost estimates [7,8]. For offshore wind, the cost reduction trend was instead taken from a technology-speci c IRENA report on the future of wind [27] since it is not featured in [9]. The resulting cost projections are presented in Table 3 and Figure 2. It is assumed that costs fall linearly between the data points provided by IRENA and that costs remain constant beyond 2040 when the IRENA forecasts end (except for offshore wind, where the IRENA forecast continues to 2050). Fixed costs for renewable energy technologies in each year were estimated by calculating a certain percentage (ranging from 1-4% depending on the technology) of the capital cost in that year, as done by IRENA [9].
Country-speci c capacity factors for solar PV, onshore wind and hydropower were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset [3,10,11]. These sources provide hourly capacity factors for 2015 for solar PV and wind, and 15-year average monthly capacity factors for hydropower, the average values of which are presented in Table 2. Country-speci c capacity factors for offshore wind were estimated based on an NREL source that gives estimates of the potential wind power capacity by capacity factor range in each country [22], from which a capacity-weighted average was calculated. The capacity factor 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 a reports by IRENA [7], which provides estimated capacity factors for ASEAN. The combined capital costs of power transmission and distribution are estimated based on an ERIA report which gives estimated capital costs for 9 projects in ASEAN [14], with an average value used. The xed operational cost is assumed to be 2% of the estimated capital cost, as done by ERIA [14]. The combined losses of transmission and distribution in 2014 were sourced from IEA data [13], and it was then assumed that combined losses would fall to 5% by 2050 in a linear fashion from 2014. Techno-economic data for re neries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) [16], which provides generic estimates of costs and performance parameters, while the re nery options modelled are based on the methods used in The Electricity Model Base for Africa [28].

Fuel Data
Fuel prices for crude oil, diesel, fuel oil, natural gas and coal were taken from the APEC Energy Outlook 7th Edition [17], which provides cost estimates by fuel from 2016 to 2050. APEC provide different natural gas and coal prices for net importers, exporters, and neutral countries, with the relevant prices used for the country. The domestic biomass price was estimated from an ERIA report that gives a local average in Thailand [18], since this was the most region-speci c cost estimate that could be sourced. The imported biomass price is an international average taken from a 2021 biomass markets report by Argus Media [19].

Emissions Factors and Domestic Reserves
Emissions factors were collected from the IPCC Emission Factor Database [20], which provides carbon emissions factors by fuel. The domestic solar and wind resources were collected from NREL datasets, which provide estimates of potential yearly generation by country [12,21]. Other renewable energy potentials were sourced from a regional report [22], country-level study [23], and the World Small Hydropower Development Report [24], which provide estimated potentials by country. Estimated domestic fossil fuel reserves were sourced from Worldometer [25], which provides estimates of reserves by country.

Electricity Demand Data
The nal electricity demand projection was determined by applying the BAU electricity demand growth trend from neighboring  Projected costs of renewable energy technologies for selected years to 2050 [7,8,9]

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.