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Configuration

This chapter compares the default PyPSA-Earth configuration to the PyPSA-ASEAN configuration.

Methodology

PyPSA-ASEAN is a regional adaptation of the open-source PyPSA-Earth framework 2. It builds on PyPSA-Earth’s preprocessing routines and optimisation capabilities, while leveraging ASEAN-specific data on power plants, renewable resources, transmission infrastructure, and electricity demand. Inclusion of customized data, on top of the PyPSA-Earth compatible databases, allowed the model to represent the region’s electricity system and enable the optimization of least-cost investment and dispatch of generation, storage, and transmission assets.

renewable-pot

Figure: Solar (left) and wind (right) potential in GWh per km² based on ERA5 Copernicus climate data for the weather year of 2013. Data is processed by Atlite and clustered by the closest OpenStreetMaps substation.

In this work, input data was compiled from multiple open-source databases to ensure high resolution and consistency.

  • Renewable generation potentials were estimated using Atlite, which processes Copernicus ERA5 climate data to generate hourly time series for onshore and offshore wind, solar PV, and hydropower 3, as shown in the Figure above.
  • Rooftop solar PV potential was estimated using Obane’s methodology 4, which combines GIS-derived rooftop areas with installation ratios from related literature.
  • Power plant data were gathered using the powerplantmatching Python package 5, which consolidated and cross-validated datasets, primarily from Global Energy Monitor.
  • Renewable energy infrastructure from IRENA 6 were incorporated and allocated according to regional resource potential. Transmission network data are derived from OpenStreetMap 7 and national power development plans 8 9 10 11 12 13 14 15, including all 2025 infrastructure (planned or under construction) to ensure internal connectivity within each country, with the exception of Indonesia’s Supergrid, as visualised in Figure 2.
  • Technology cost assumptions combine PyPSA-Earth’s default database and data from AEO8 1.
  • Electricity demand growth followed the AEO8’s Baseline Scenario 1 with demand projections disaggregated two-fold: first, by country based on UN population projections 16, and second, nodally within countries based on historical rasterized population and GDP data (adapted from PyPSA-Earth 2).

System expansion was simulated from 2025 to 2050 at five-year intervals. With this, investment decisions were determined sequentially using a myopic approach, in which capacity expansion in each period was optimised based on prevailing system conditions without knowledge of future periods. This approach reflected practical planning constraints and captured stage-wise development of generation, storage, and transmission assets.

References

  1. ASEAN Centre for Energy (ACE) 2024 8th ASEAN Energy Outlook (AEO8) Vol. 8 ISSN 2963-539X revised November 2024 Jakarta: ASEAN Centre for Energy Available at https://aseanenergy.org/ ↩↩

  2. Parzen M, Abdel-Khalek H, Fedotova E, Mahmood M, Frysztacki M M, Hampp J, Franken L, Schumm L, Neumann F, Poli D, Kiprakis A and Fioriti D 2023 PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa Applied Energy 341 121096 ↩↩

  3. Hofmann F, Hampp J, Neumann F, Brown T and Hörsch J 2021 atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series Journal of Open Source Software 6(62) 3294 ↩

  4. Obane H 2025 Exploring the technical potential of solar PV and wind energy system in ASEAN The Institute of Energy Economics Japan ↩

  5. Gotzens F Heinrichs H Hörsch J and Hofmann F 2019 Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases Energy Strategy Reviews 23 1–12 ↩

  6. IRENA 2025 Renewable energy statistics 2025 International Renewable Energy Agency ↩

  7. OpenStreetMap contributors 2025 Planet dump https://www.openstreetmap.org ↩

  8. Électricité du Cambodge 2018 Électricité du Cambodge Annual Report. https: //www.edc.com.kh/annually_page/annuallyReport ↩

  9. Direktorat Jenderal Ketenagalistrikan 2021 RUPTLN 2021-2030. Direktorat Jenderal Ketenagalistrikan. Sept. 28, 2021. URL: https://gatrik.esdm.go.id/assets/ uploads/download_index/files/38622- ruptl- pln- 2021- 2030.pdf ↩

  10. Kimura S and Ueda K 2021 Feasibility Study on the Transmission Highway in ACMECS Economic Research Institute for ASEAN and East Asia ↩

  11. Sarawakenergy. Sarawak’s Power Generation and Network https://www.sarawakenergy.com/assets/images/power-generation/map.png ↩

  12. Ministry of Electric Power (MOEP) 2019. Existing Power Grid and Under Construction Projects https://moep.gov.mm/en/ignite/page/641 ↩

  13. National Grid Corporation of the Philippines 2020 NGCP Transmission Developement Plan 2020-2040 Consultation Draft https://www.ngcp.ph/Attachment- Uploads/TDP%202020- 2040%20Consultation%20Draft%20Volume%201%20Major%20Network%20Development_2020-02-10-17-38-50.pdf ↩

  14. Global Energy Network Institue. Singapore Energy Summary https://www.geni.org/globalenergy/library/national_energy_grid/singapore/singaporeannationalelectricitygrid.shtml ↩

  15. Energy Comission of Sabah 2023 Sabah Energy Roadmap and Master Plan 2040 https://ecos.gov.my/sites/default/files/uploads/downloads/202309/SABAH%20ENERGY%20ROADMAP%20AND%20MASTER%20PLAN%202040%20%28SERAMP%202040%29.pdf ↩

  16. United Nations, Department of Economic and Social Affairs, Population Division 2024 World Population Prospects 2024, Online Edition https://population.un.org/wpp/ ↩