Technoeconomic data and assumptions for long-term energy systems modelling in Indonesia

Indonesia's emission reduction commitment and clean energy transition target emphasises the importance of energy system modelling for analysing and projecting Indonesia's capacity, resource availability, and future conditions in achieving these objectives. Utilising energy systems modelling based on adequate and reliable data enables policymakers to select the most optimal alternatives in energy planning. Aligned with the U4RIA (Ubuntu, Retrievability, Repeatability, Reconstructability, Interoperability, Auditability) concept, this database may facilitate various related stakeholders in obtaining this comprehensive and detailed energy data, while the data gathering and processing can also be applied to other developing countries. This country-specific dataset covers the historical data of electricity generation, demand, installed capacity, capacity factor, technical lifetime, renewable energy potentials, costs, and its projections up to 2050. The data in this article is ready to be used for energy system and modelling research.


Subject
Energy Specific subject area Energy system modelling Type of data Tables and graphs.Data collection Data were collected from publicly accessible annual reports and databases from different energy related national institutions in Indonesia, as well as existing modelling databases.The annual reports and databases are available on related institutions' website, listed on the Reference section.Data source location Raw data sources are listed in the different sections of this article, including previous dataset [8] and national institutions: • Ministry of Energy and Mineral Resources, Jakarta, Indonesia [ 8 , 9 ] • Indonesia's National Energy Council, Jakarta, Indonesia [11] Data

Value of the Data
• This dataset can be used to develop energy system models and explore clean energy transition pathways in Indonesia.Incorporating this with various scenario framework and hypotheses may provide insights to policymakers.• The data are open-source, comprehensive, and accessible, addressing challenges associated with complex and time-consuming data collection process.• The data are useful for energy modellers, analysts, researchers, policymakers, and other related stakeholders as a foundation for model development and analysis in the energy sector.Moreover, the energy system analysis result enables governments to strategically allocate financial resources for implementation, define the role of public funds, and enhance accessibility to global climate finance [1] .

Background
Energy systems modelling plays a crucial role in providing information and insights for policymakers.However, obtaining accurate and reliable data for national-scale modelling poses challenges due to inaccessibility and inconsistency, with an addition of costly energy modelling tools [2] .Thus, the present study addresses the aforementioned data gap, presenting energy data and assumptions for long-term energy planning in Indonesia that may be utilised by stakeholders from academia, public, and private sectors.In compliance with U4RIA concept of Ubuntu, Retrievability, Repeatability, Reconstructability, Interoperability, Auditability [3] , this paper aims to improve energy modelling to support policy and decision-making in energy sector.This datain-brief is the dataset used for research titled "Reducing Fossil Fuel Dependence and Exploring Just Energy Transition Pathways in Indonesia using OSeMOSYS (Open-Source Energy Modelling System)" [4] which focused on the data published by national institutions and existing model databases, as opposed to generic data from international organisations, to avoid inconsistency.This dataset may also be a starting point for future databases of other emerging nations, to be integrated to a new starter data-kit and building up the existing Starter Data Kits library [5] with a user-friendly ClicSAND (Simple and Nearly Done) interface [6] .

Data Description
This article presents national datasets of Indonesia that can be utilised for energy modelling of a long-term decarbonisation and clean energy transition planning in OSeMOSYS tool.However, it is important to note that the data provided in this document exist independently of the tool.To enhance accessibility, the dataset can be accessed on Zenodo repository through the following link: https://zenodo.org/records/10369495[7] .The data is sourced from publicly accessible sources, such as national institutions in Indonesia and pre-existing model databases.This contains information of costs (capital and fixed), capacity factor, technical life of power plants, electricity production and demand, installed capacity, and renewable energy supply and potential in 2015-2050, categorised into 9 technologies of existing power generators in Indonesia, defined in the excel file of repository under the sheet 'Sets'.

Electricity demand and capacity factor
Electricity demand data is sourced from pre-existing model database by Paiboonsin (2023) [8] .Electricity demand is classified through 3 (three) sectors, industrial, residential, and commercial, as shown on Table 1 and Fig. 1 .The demand is forecasted to increase by 2-5 % annually.The data is available in the excel file of repository under the sheet 'Demand'.Capacity factor represents the overall utilisation ratio of a power generator, through the energy generated over a given timeframe compared to its full capacity.Capacity factor is estimated through a calculation and assumption listed on Section 3.1 from a pre-existing model dataset [8] .The data is available on Table 2 and in the excel file of repository under the sheet 'Capacity Factor & Demand'.

Capital and fixed costs
The capital and fixed cost data is mainly sourced from Ministry of Energy and Mineral Resources (MEMR) of Indonesia's report "Technology Data for the Indonesian Power Sector: Catalogue for Generation and Storage of Electricity" [9] .The data stated on MEMR's report include data of biomass, coal, geothermal, light fuel oil, CCGT, SCGT, solar PV (utility), all hydropower types, onshore and offshore wind of 2020, 2030, and 2050.Meanwhile data of other technologies is obtained from Paiboonsin (2023) [8] .The complete data is available in the excel file of repository under the sheet 'Capital Cost' and 'Fixed Cost'.

Technical lifetime
The technical lifetime defines the typical length of power plant's operational years.The technical lifetime data is obtained from Ministry of Energy and Mineral Resources (MEMR)'s report [9] for biomass, coal, geothermal, light fuel oil, CCGT, SCGT, solar PV (utility), all hydropower types, onshore and offshore wind.Data of other technologies are obtained from previous model dataset [8] .The complete data is available in the excel file of repository under the sheet 'Operational Life'.The data is listed on Table 5 .

Residual capacity
Residual capacity is annual installed capacity of power generation technologies.The on-grid residual capacity data of 2015-2021 is sourced from MEMR's "Electricity Statistics of Indonesia" report [10] .Meanwhile the remaining years and some of the available off-grid technologies, also the electricity transmission and distribution capacity are obtained from existing model dataset [8] .The complete data is available in the excel file of repository under the sheet 'Residual Capacity'.

Renewable energy potential
Renewable energy resources in Indonesia includes, but not limited to, geothermal, bioenergy, wind, hydropower, and solar technology.The available renewable energy potential and realisation data is obtained from National Energy Council of Indonesia's "Indonesia Energy Outlook" report [11] .The data is also available in the excel file of repository under the sheet 'RE Supply & Potential'.

Experimental Design, Materials and Methods
Data is collected through secondary data collection and literature review.From the preexisting model dataset, this updates data from national institutions including Ministry of Energy and Mineral Resources (MEMR) and National Energy Council (NEC) of Indonesia.The raw data is then analysed and processed as the input for the energy modelling.Data sources and process methods are as follows.

Electricity demand and capacity factor
Electricity demand data is a raw data obtained from Paiboonsin [8] , meanwhile capacity factor data is derived from a calculation of time slices reduction from 8 [8] to 4, representing existing season variations in Indonesia as an equatorial country (dry season-November to March and wet season-April to October) and daily load period (day-06 a.m.to 06 p.m. and night-06 p.m. and 06 a.m.).Thus, obtaining 4 time slices of S101 (Dry Day), S102 (Dry Night), S103 (Wet Day), and S104 (Wet Night).Time slices reduction calculation refers to Cannone et al. (2022) [12] .

Capital and fixed costs
Capital and fixed costs data in this study refers to the raw data available on Indonesia's Ministry of Energy and Mineral Resources (MEMR)'s report "Technology Data for the Indonesian Power Sector: Catalogue for Generation and Storage of Electricity" [9] , where data of 2020, 2030, and 2050 are available, while other years are assumed to be constant over the values of previously available years.

Technical lifetime, electricity production, renewable energy potential
Technical lifetime, electricity production, and renewable energy potential data are raw data from national institutions and further analysed for this study.The data of technical lifetime is sourced from MEMR's report "Technology Data for the Indonesian Power Sector: Catalogue for Generation and Storage of Electricity" [9] .Renewable energy potential data is sourced from National Energy Council of Indonesia's "Indonesia Energy Outlook" report [11] .Meanwhile historical electricity production data until 2021 is the raw data obtained from MEMR's "Electricity Statistics of Indonesia" report [10] in Gigawatt-Hour (GWh), and then processed to be consistent with the unit used in OSeMOSYS, Petajoule (PJ).

Residual capacity
Historical residual capacity data until 2021 is sourced from MEMR's report "Electricity Statistics of Indonesia" [10] for biomass, coal, geothermal, light fuel oil, oil (SCGT), gas CCGT, gas SCGT, solar PV (utility), onshore wind, and hydro.For data from 2022 for all technologies and solar PV (distributed with storage), off-grid hydropower, electricity transmission and distribution are obtained from pre-existing model database [8] .

Limitations
The limitation includes the limited data on the off-grid electricity production and power plant capacity, and thus this article focuses mainly on the available on-grid data with a few off-grids.Incorporating other off-grid technologies may provide a more comprehensive energy modelling analysis for long-term plan.

Table 1
Total Electricity Demand of Key Years (PJ).

Table 2
Capacity Factor of Power Generation Technologies.

Table 6
Residual capacity of key years (GW/year).

Table 7
Historical electricity production of power generator technologies (PJ).

Table 8
Renewable energy potential and realisation (GW).