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

Selected ‘Starter Kit’ energy system modelling data for Taiwan (#CCG) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Data Note Selected ‘Starter Kit’ energy system modelling data for Taiwan (#CCG) Carla Cannone, Lucy Allington, Ioannis Pappis, Karla Cervantes Barron, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-757733/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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 Taiwan, 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 Engineering U4RIA Renewable energy Cost-optimization Taiwan Energy policy CCG OSeMOSYS Figures Figure 1 Figure 2 Figure 3 Figure 4 Specifications Table Subject Energy Specific subject area Energy System Modelling Type of data Tables Graphs Charts Description of modelling assumptions How data were acquired Literature survey (databases and reports from international organisations; journal articles) Data format Raw and Analysed Parameters for data collection Data collected based on inputs required to create an energy system model for Taiwan Description of data collection Data were collected from the websites, annual reports and databases of international organisations, as well as from academic articles and existing modelling databases. Data source location Not applicable Data accessibility With the article and in a repository. Repository name: Zenodo. Data identification number: v1.0.0. Direct URL to data: http://doi.org/10.5281/zenodo.5139521 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 scientific 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]. 1 Data Description The data provided in this paper can be used as input data to develop an energy system model for Taiwan. 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 datafiles 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 final electricity demands. Below shows the different items and their description, in order of appearance, presented in this article. Item Description of Content Table 1 A table showing the estimated installed capacity of different power plant types in Taiwan for 2015-2018 Table 2 A table showing techno-economic parameters for electricity generation technologies Table 3 A table showing capital cost projections for renewable energy technologies up to 2050 Figure 2 A graph showing capital cost projections for renewable energy technologies from 2015-2050 Table 4 A table showing cost and performance parameters for power transmission and distribution technologies Table 5 A table showing cost and performance data for refinery technologies Table 6 A table showing fuel price projections up to 2050 Figure 3 A graph showing fuel price projections from 2015-2050 Table 7 A table showing carbon dioxide emissions factors by fuel Table 8 A table showing estimated renewable energy potential in Taiwan Table 9 A table showing estimated fossil fuel reserves in Taiwan Figure 4 A graph showing a final electricity demand projection for Taiwan from 2015-2070 1.1 Existing Electricity Supply System The total power generation capacity in Taiwan is estimated at 46109.64 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. Table 1: Installed Power Plants Capacity in Taiwan [3,4,5,6] Electricity Generation Technology Estimated Installed Capacity (MW) 2015 2016 2017 2018 Biomass Power Plant 740.0 740.0 740.0 740.0 Coal Power Plant 18650.42 18650.42 18650.42 18650.42 Oil Fired Gas Turbine (SCGT) 2631.94 2631.94 2631.94 2631.94 Gas Power Plant (CCGT) 13560.64 13560.64 13560.64 13560.64 Solar PV (Utility) 842.0 842.0 842.0 842.0 Large Hydropower Plant (Dam) (>100MW) 3778.0 3778.0 3778.0 3778.0 Medium Hydropower Plant (10-100MW) 41.0 41.0 41.0 41.0 Small Hydropower Plant (<10MW) 3.0 3.0 3.0 3.0 Onshore Wind 646.64 646.64 646.64 646.64 Nuclear Power Plant 5216.0 5216.0 5216.0 5216.0 Total Capacity 46109.64 46109.64 46109.64 46109.64 1.2 Techno-economic Data for Electricity Generation Technologies The techno-economic parameters of electricity generation technologies are presented in Table 2, including costs, operational lives, efficiencies and average capacity factors. Cost (capital and fixed), operational life and efficiency 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-specific 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. Country-specific capacity factors for solar PV, wind and hydropower technologies in Taiwan 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. Table 2: Techno-economic parameters of electricity generation technologies [3,7,8,9,10,11,12] Technology Capital Cost ($/kW in 2020) Fixed Cost ($/kW/yr in 2020) Operational Life (years) Efficiency Average Capacity Factor Biomass Power Plant 2750.0 69.0 25 0.38 0.7 Coal Power Plant 1300.0 52.0 60 0.3 0.75 Geothermal Power Plant 2500.0 100.0 50 0.1 0.7 Light Fuel Oil Power Plant 1200.0 18.0 50 0.4 0.25 Oil Fired Gas Turbine (SCGT) 1344.0 18.0 50 0.4 0.25 Gas Power Plant (CCGT) 1000.0 40.0 30 0.55 0.55 Gas Power Plant (SCGT) 784.0 23.0 30 0.35 0.55 Solar PV (Utility) 1160.0 15.08 30 1.0 0.24 CSP with Storage 4965.31 120.0 35 0.33 0.3 Large Hydropower Plant (Dam) (>100MW) 1539.0 46.17 40 1.0 0.19 Medium Hydropower Plant (10-100MW) 1592.86 47.79 40 1.0 0.19 Small Hydropower Plant (<10MW) 2162.0 64.86 40 1.0 0.19 Onshore Wind 2220.09 88.8 30 1.0 0.27 Offshore Wind 2876.21 115.05 30 1.0 0.34 Nuclear Power Plant 5500.0 138.0 60 0.33 0.83 Light Fuel Oil Standalone Generator (1kW) 1500.0 38.0 20 0.42 0.4 Solar PV (Distributed with Storage) 2130.8 42.62 24 1.0 0.24 Table 3: Projected costs of renewable energy technologies for selected years to 2050. [7,8,9] Renewable Energy Technology Capital Cost ($/kW) 2015 2020 2025 2030 2040 2050 Biomass Power Plant 2750.0 2750.0 2750.0 2750.0 2750.0 2750.0 Solar PV (Utility) 1822.5 1160.0 828.33 745.83 608.62 608.62 CSP with Storage 7404.71 4965.31 4000.0 3223.13 3134.9 3134.9 Large Hydropower Plant (Dam) (>100MW) 1539.0 1539.0 1539.0 1539.0 1539.0 1539.0 Medium Hydropower Plant (10-100MW) 1592.86 1592.86 1592.86 1592.86 1592.86 1592.86 Small Hydropower Plant (<10MW) 2162.0 2162.0 2162.0 2162.0 2162.0 2162.0 Onshore Wind 2959.63 2220.09 1775.78 1620.71 1391.1 1391.1 Offshore Wind 3620.25 2876.21 2187.28 1773.92 1647.21 1520.5 Solar PV (Distributed with Storage) 3502.0 2130.8 1880.8 1755.8 1690.8 1625.8 1.3 Techno-economic Data for Power Transmission and Distribution The combined losses in electricity transmission and distribution in Taiwan in 2014 are estimated based on a study by Singh and Kumar [13]. It was then assumed that combined losses would be reduced to 5% by 2050, falling in a linear fashion. Combined transmission and distribution efficiency in Taiwan is therefore assumed to reach 93.0% and 95.0% in 2030 and 2050 respectively. The combined costs of power tansmission and distribution are estimated based on a report by the Economic Research Institute for ASEAN and East Asia (ERIA) [14], which gives cost estimates for several real-life projects in ASEAN. For more detail, see section 2.In the following table, the techno-economic parameters associated with the transmission and distribution network are presented. Table 4: Techno-economic parameters for transmission and distribution [13,14] Technology Capital Cost ($/kW, 2020) Fixed Cost ($/kw/yr, 2020) Operational Life (years) Combined Efficiency (2020) Combined Efficiency (2030) Combined Efficiency (2050) Electricity Transmission and Distribution 306.39 6.13 50 0.92 0.93 0.95 1.4 Techno-economic Data for Refineries Taiwan has an estimated 1230kb/d domestic refinery capacity [15]. In the OSeMOSYS model, two oil refinery technologies were made available for investment in the future, each with different output activity ratios for Heavy Fuel Oil (HFO) and Light Fuel Oil (LFO). The technoeconomic data for these technologies are shown in Table 5. Table 5: Techno-economic parameters for refinery technologies [15,16] Technology Capital Cost ($/kW in 2020) Variable Cost ($/GJ in 2020) Operational Life (years) Output Ratio Crude Oil Refinery Option 1 24.1 0.71775 35 0.9 LFO : 0.1 HFO Crude Oil Refinery Option 2 24.1 0.71775 35 0.8 LFO : 0.2 HFO 1.5 Fuel Prices Assumed costs are provided for both imported and domestically-extracted fuels. The fuel price projections until 2050 are presented below. These are estimates based on Asia-specific cost estimates produced by the Asia Pacific Economic Cooperation (APEC) and ERIA [17.18], with an international average biomass price in 2020 assumed for imported biomass [19]. More detail is provided in Section 2.2. Table 6: Fuel price projections to 2050 [17,18,19] Commodity Fuel Price ($/GJ) 2015 2020 2025 2030 2040 2050 Crude Oil Imports 6.27 13.95 15.12 16.29 19.84 21.33 Crude Oil Extraction 5.7 12.68 13.75 14.81 18.03 19.39 Biomass Imports 5.55 5.55 5.55 5.55 5.55 5.55 Biomass Extraction 1.34 1.34 1.34 1.34 1.34 1.34 Coal Imports 2.38 3.03 3.09 3.15 3.53 3.61 Coal Extraction 2.16 2.72 2.77 2.82 3.18 3.25 Light Fuel Oil Imports 6.83 15.21 16.49 17.77 21.64 23.26 Heavy Fuel Oil Imports 5.99 13.3 14.43 15.55 18.94 20.35 Natural Gas Imports 5.71 9.98 10.17 10.37 10.72 10.75 Natural Gas Extraction 5.16 8.98 9.16 9.34 9.65 9.67 1.6 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. Table 7: Fuel-specific CO2 Emission Factors [20] Fuel CO2 Emission Factor (kg CO2/GJ) Crude oil 73.3 Biomass 100 Coal 94.6 Light Fuel Oil 69.3 Heavy Fuel Oil 77.4 Natural Gas 56.1 1.7 Renewable and Fossil Fuel Reserves Tables 8 and 9 show estimated domestic renewable energy potentials and fossil fuel reserves respectively in Taiwan. Table 8: Estimated Renewable Energy Potentials [12,21,22] Unit Estimated Renewable Energy Potential Solar PV TWh/yr 36.1 Onshore Wind TWh/yr 1888.6 Offshore Wind TWh/yr 73 Medium & Large Hydropower MW 25700 Geothermal MW 714 Table 9: Estimated Fossil Fuel Reserves [17] Proven Reserves Coal (million tonnes) 0.0 Crude Oil (billion barrels) 0.0 Natural Gas (trillion cubic metres) 0.0 1.8 Electricity Demand Projection Final electricity demand in Taiwan was estimated at 849.79 PJ in 2016 and is forecasted to reach 917.63 PJ by 2030 and 901.37 PJ by 2050 in a Business as Usual (BAU) scenario according to the APEC Energy Supply and Demand Outlook 7th Edition [17]. Figure 4 below shows the final electricity demand projection. 2 Experimental Design, Materials, And Methods Data were primarily collected from the reports and websites of international organizations, including the International Renewable Energy Agency (IRENA), the Asia Pacific Economic Cooperation (APEC), the Economic Research Institute for ASEAN and East Asia (ERIA), the International Energy Agency (IEA), and the Intergovernmental Panel on Climate Change (IPCC). The data sources used are detailed in this section. 2.1 Electricity Supply System Data Data on Taiwan'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 [23]. 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. Cost, efficiency 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-specific cost reduction trends from a very recent IRENA report focussing on Africa [9] were applied to the current Asia-specific cost estimates [7,8]. For offshore wind, the cost reduction trend was instead taken from a technology-specific IRENA report on the future of wind [25] 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-specific 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-specific 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 fixed 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 a study by Singh and Kumar [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 refineries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) [16], which provides generic estimates of costs and performance parameters, while the refinery options modelled are based on the methods used in The Electricity Model Base for Africa [25]. 2.2 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-specific 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]. 2.3 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 wind resources were collected from an NREL dataset, which provides estimates of potential yearly generation by country [12]. Other renewable energy potentials were sourced from national studies [21,22]. Estimated domestic fossil fuel reserves were sourced from the APEC Energy Outlook 7th Edition [17], which provides estimates of reserves by country. Although Taiwan may have some domestic coal resources, an estimate could not be sourced since reserves have not been estimated since production stopped in 2001 [17]. 2.4 Electricity Demand Data The final electricity demand projection is based on the BAU projection from the APEC Energy Outlook 7th Edition [17], which provides total demand estimates for every five years from 2015 to 2050, with demand assumed to change linearly between these data points. 3 Ethics Statement Not applicable. 4 Credit Author Statement Lucy Allington: Data curation; Investigation; Methodology; Writing – original draft; Visualisation. Carla Cannone: Data curation; Investigation; Software; Formal analysis; Visualisation. Ioannis Pappis: Data curation; Investigation; Validation; Writing - Review & Editing. Karla Cervantes Barron: Data Curation; Software; Visualisation. William Usher: Software; Supervision. Steve Pye: Supervision; Project Administration. Edward Brown: Funding Acquisition. Mark Howells: Conceptualisation; Methodology; Writing – Review & Editing; Supervision. Miriam Zachau Walker: Software. Aniq Ahsan: Software. Flora Charbonnier: Software. Claire Halloran: Software. Stephanie Hirmer: Supervision; Writing - Review & Editing. Constantinos Taliotis: Conceptualisation; Writing - Review & Editing. Caroline Sundin: Conceptualisation; Writing - Review & Editing. Vignesh Sridharan: Conceptualisation. Eunice Ramos: Conceptualisation. Maarten Brinkerink: Data curation. Paul Deane: Data Curation. Gustavo Moura: Data Curation. Arnaud Rouget: Conceptualisation. Andrii Gritsevskyi: Conceptualisation. David Wogan: Conceptualisation. Edito Barcelona: Conceptualisation. Holger Rogner: Conceptualisation. Jennifer Cronin: Writing - Review & Editing. Declarations Acknowledgements We would like to acknowledge data providers who helped make this, and future iterations possible, they include IEA, UNSTATS, APEC, IRENA, UCC, KTH, UFOP and others. Funding As well as support in kind provided by the employers of the authors of this note, we also acknowledge core funding from the Climate Compatible Growth Program (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government’s official policies. Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. References Cannone C. Towards evidence-based policymaking: energy modelling tools for sustainable development [Projecte Final de Màster Oficial]. UPC, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona, Departament d'Enginyeria Química; 2020. http://hdl.handle.net/2117/333306 Howells M, Quiros-Tortos J, Morrison R, Rogner H, Niet T, Petrarulo L, et al. 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Energy Policy. 2011 Oct 1;39(10):5850–70. Allington, L., Cannone, C., Pappis, I., Cervantes Barron, K., Usher, W., et al. (2021). CCG Starter Data Kit: Taiwan. (Version v1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5139521 Allington L., Cannone C., Hooseinpoori P., Kell A., Taibi E., Fernandez C., Hawkes A., Howells M, 2021. Energy and Flexibility Modelling. Release Version 1.0 [online course]. Climate Compatible Growth Programme and the International Renewable Energy Agency. https://www.open.edu/openlearncreate/course/view.php?id=6817 Supplementary Files TaiwanSupplementaryMaterials.zip Model text files for the scenarios described in Appendix A Appendix.docx Appendix A – Zero-Order Tier 2 OSeMOSYS Model Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-757733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":42266566,"identity":"42cb4fc2-330b-4993-8f1c-6df0d5981b3e","order_by":0,"name":"Carla 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paper.","description":"","filename":"FigA3TWN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/0c64fbe9876a07a0a1e85bd8.jpg"},{"id":11889310,"identity":"e33f9e57-bc5f-43ed-86d2-691e7db6cec0","added_by":"auto","created_at":"2021-07-28 17:53:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72752,"visible":true,"origin":"","legend":"Projected costs of renewable energy technologies for selected years to 2050 [7,8,9] ","description":"","filename":"Fig1TWN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/ce3cf9441fc11db43a6f1b27.jpg"},{"id":11889894,"identity":"098189dc-1449-4132-ae1e-2d8039c6959c","added_by":"auto","created_at":"2021-07-28 17:59:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85059,"visible":true,"origin":"","legend":"Fuel price projections to 2050 [17,18,19] ","description":"","filename":"Fig2TWN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/f677b8571b5a59b92a521cff.jpg"},{"id":11889314,"identity":"b91652a3-6a5c-4659-8634-bd2c16452eaa","added_by":"auto","created_at":"2021-07-28 17:53:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28704,"visible":true,"origin":"","legend":"Final Electricity Demand Projection (PJ) [17] ","description":"","filename":"Fig3TWN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/60a96237170860e7b93a4c5b.jpg"},{"id":13706450,"identity":"9f4707f9-a0d1-4fdb-8d72-3ff9cbee56cf","added_by":"auto","created_at":"2021-09-17 13:57:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/6fe727f4-5e05-469b-92dc-9722f3e29207.pdf"},{"id":11889313,"identity":"aa8b9d42-c146-45dc-b0de-283edc2957e0","added_by":"auto","created_at":"2021-07-28 17:53:42","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":616736,"visible":true,"origin":"","legend":"Model text files for the scenarios described in Appendix A","description":"","filename":"TaiwanSupplementaryMaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/17edf1f944d6a6eb02af1d11.zip"},{"id":11889634,"identity":"646ba8ba-9dbd-42c9-b975-3924c296ee1a","added_by":"auto","created_at":"2021-07-28 17:56:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":911698,"visible":true,"origin":"","legend":"Appendix A – Zero-Order Tier 2 OSeMOSYS Model ","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-757733/v1/54717728a24aa701c8c97ba1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSelected ‘Starter Kit’ energy system modelling data for Taiwan (#CCG) \u003c/p\u003e","fulltext":[{"header":"Specifications Table","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific subject area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy System Modelling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTables\u003c/p\u003e \u003cp\u003e Graphs\u003c/p\u003e \u003cp\u003e Charts\u003c/p\u003e \u003cp\u003e Description of modelling assumptions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow data were acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature survey (databases and reports from international organisations; journal articles)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData format\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw and Analysed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters for data collection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData collected based on inputs required to create an energy system model for Taiwan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription of data collection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData were collected from the websites, annual reports and databases of international organisations, as well as from academic articles and existing modelling databases.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData source location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData accessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith the article and in a repository. Repository name: Zenodo. Data identification number: v1.0.0. Direct URL to data: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.5281/zenodo.5139521\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eValue of the data\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThese 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.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe data are useful for country analysts, policy makers and the broader scientific community, as a zero-order starting point for model development.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThese 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.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe 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].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"1 Data Description","content":"\u003cp\u003eThe data provided in this paper can be used as input data to develop an energy system model for Taiwan. 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 datafiles 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.\u003c/p\u003e\n\u003cp\u003eThe 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 final electricity demands. Below shows the different items and their description, in order of appearance, presented in this article.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDescription of Content\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing the estimated installed capacity of different power plant types in Taiwan for 2015-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing techno-economic parameters for electricity generation technologies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing capital cost projections for renewable energy technologies up to 2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFigure 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA graph showing capital cost projections for renewable energy technologies from 2015-2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing cost and performance parameters for power transmission and distribution technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing cost and performance data for refinery technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing fuel price projections up to 2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFigure 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA graph showing fuel price projections from 2015-2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing carbon dioxide emissions factors by fuel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing estimated renewable energy potential in Taiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTable 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA table showing estimated fossil fuel reserves in Taiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFigure 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eA graph showing a final electricity demand projection for Taiwan from 2015-2070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.1 Existing Electricity Supply System\u003c/h2\u003e\n\u003cp\u003eThe total power generation capacity in Taiwan is estimated at 46109.64 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.\u003c/p\u003e\n\u003cp\u003eTable 1: Installed Power Plants Capacity in Taiwan [3,4,5,6]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"19.96527777777778%\"\u003e\n \u003cp\u003eElectricity Generation Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"80.03472222222223%\"\u003e\n \u003cp\u003eEstimated Installed Capacity (MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBiomass Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e740.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e740.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e740.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e740.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eCoal Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e18650.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e18650.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e18650.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e18650.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eOil Fired Gas Turbine (SCGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2631.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2631.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2631.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2631.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eGas Power Plant (CCGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e13560.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e13560.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e13560.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e13560.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eSolar PV (Utility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e842.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e842.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e842.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e842.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eLarge Hydropower Plant (Dam) (\u0026gt;100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3778.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3778.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3778.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3778.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eMedium Hydropower Plant (10-100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eSmall Hydropower Plant (\u0026lt;10MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eOnshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e646.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e646.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e646.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e646.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eNuclear Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e5216.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e5216.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e5216.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e5216.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eTotal Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e46109.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e46109.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e46109.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e46109.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.2 Techno-economic Data for Electricity Generation Technologies\u003c/h2\u003e\n\u003cp\u003eThe techno-economic parameters of electricity generation technologies are presented in Table 2, including costs, operational lives, efficiencies and average capacity factors. Cost (capital and fixed), operational life and efficiency 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-specific 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. Country-specific capacity factors for solar PV, wind and hydropower technologies in Taiwan 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.\u003c/p\u003e\n\u003cp\u003eTable 2: Techno-economic parameters of electricity generation technologies [3,7,8,9,10,11,12]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eCapital Cost ($/kW in 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eFixed Cost ($/kW/yr in 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eOperational Life (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eAverage Capacity Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eBiomass Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eCoal Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1300.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eGeothermal Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2500.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLight Fuel Oil Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1200.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eOil Fired Gas Turbine (SCGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1344.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eGas Power Plant (CCGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1000.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eGas Power Plant (SCGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e784.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eSolar PV (Utility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1160.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eCSP with Storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4965.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLarge Hydropower Plant (Dam) (\u0026gt;100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e46.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMedium Hydropower Plant (10-100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e47.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eSmall Hydropower Plant (\u0026lt;10MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e64.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eOnshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2220.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e88.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eOffshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2876.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e115.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNuclear Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e5500.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e138.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLight Fuel Oil Standalone Generator (1kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1500.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eSolar PV (Distributed with Storage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2130.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e42.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Projected costs of renewable energy technologies for selected years to 2050. [7,8,9]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"15.986394557823129%\"\u003e\n \u003cp\u003eRenewable Energy Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"84.01360544217687%\"\u003e\n \u003cp\u003eCapital Cost ($/kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eBiomass Power Plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2750.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eSolar PV (Utility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1822.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1160.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e828.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e745.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e608.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e608.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eCSP with Storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e7404.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e4965.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e4000.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e3223.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e3134.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e3134.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eLarge Hydropower Plant (Dam) (\u0026gt;100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1539.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eMedium Hydropower Plant (10-100MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1592.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eSmall Hydropower Plant (\u0026lt;10MW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2162.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eOnshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2959.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2220.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1775.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1620.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1391.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1391.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eOffshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e3620.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2876.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2187.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1773.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1647.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1520.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.040955631399317%\"\u003e\n \u003cp\u003eSolar PV (Distributed with Storage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e3502.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e2130.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1880.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1755.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1690.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.993174061433447%\"\u003e\n \u003cp\u003e1625.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.3 Techno-economic Data for Power Transmission and Distribution\u003c/h2\u003e\n\u003cp\u003eThe combined losses in electricity transmission and distribution in Taiwan in 2014 are estimated based on a study by Singh and Kumar [13]. It was then assumed that combined losses would be reduced to 5% by 2050, falling in a linear fashion. \u0026nbsp; Combined transmission and distribution efficiency in Taiwan is therefore assumed to reach 93.0% and 95.0% in 2030 and 2050 respectively. The combined costs of power tansmission and distribution are estimated based on a report by the Economic Research Institute for ASEAN and East Asia (ERIA) [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.\u003c/p\u003e\n\u003cp\u003eTable 4: Techno-economic parameters for transmission and distribution [13,14]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.89189189189189%\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.58108108108108%\"\u003e\n \u003cp\u003eCapital Cost ($/kW, 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.29054054054054%\"\u003e\n \u003cp\u003eFixed Cost ($/kw/yr, 2020)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.202702702702704%\"\u003e\n \u003cp\u003eOperational Life (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003eCombined\u0026nbsp;Efficiency (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003eCombined\u0026nbsp;Efficiency (2030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003eCombined\u0026nbsp;Efficiency (2050)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.89189189189189%\"\u003e\n \u003cp\u003eElectricity Transmission and Distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.58108108108108%\"\u003e\n \u003cp\u003e306.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.29054054054054%\"\u003e\n \u003cp\u003e6.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.202702702702704%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.344594594594595%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.4 Techno-economic Data for Refineries\u003c/h2\u003e\n\u003cp\u003eTaiwan has an estimated 1230kb/d domestic refinery capacity [15]. In the OSeMOSYS model, two oil refinery 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.\u003c/p\u003e\n\u003cp\u003eTable 5: Techno-economic parameters for refinery technologies [15,16]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eCapital Cost ($/kW in 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eVariable Cost ($/GJ in 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eOperational Life (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eOutput Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eCrude Oil Refinery Option 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.71775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.9 LFO : 0.1 HFO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eCrude Oil Refinery Option 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.71775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.8 LFO : 0.2 HFO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.5 Fuel Prices\u003c/h2\u003e\n\u003cp\u003eAssumed 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-specific cost estimates produced by the Asia Pacific 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.\u003c/p\u003e\n\u003cp\u003eTable 6: Fuel price projections to 2050 [17,18,19]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"14.974182444061961%\"\u003e\n \u003cp\u003eCommodity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"85.02581755593803%\"\u003e\n \u003cp\u003eFuel Price ($/GJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eCrude Oil Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e19.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e21.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eCrude Oil Extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e13.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e19.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eBiomass Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eBiomass Extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eCoal Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eCoal Extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eLight Fuel Oil Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e15.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e17.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e21.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eHeavy Fuel Oil Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e20.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eNatural Gas Imports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.025906735751295%\"\u003e\n \u003cp\u003eNatural Gas Extraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.162348877374784%\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.6 Emission Factors\u003c/h2\u003e\n\u003cp\u003eFossil 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.\u003c/p\u003e\n\u003cp\u003eTable 7: Fuel-specific CO2 Emission Factors [20]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFuel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eCO2 Emission Factor (kg CO2/GJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eCrude oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e73.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eBiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eCoal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e94.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eLight Fuel Oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eHeavy Fuel Oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eNatural Gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.7 Renewable and Fossil Fuel Reserves\u003c/h2\u003e\n\u003cp\u003eTables 8 and 9 show estimated domestic renewable energy potentials and fossil fuel reserves respectively in Taiwan.\u003c/p\u003e\n\u003cp\u003eTable 8: Estimated Renewable Energy Potentials [12,21,22]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003eEstimated Renewable Energy Potential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003eSolar PV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eTWh/yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003eOnshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eTWh/yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003e1888.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003eOffshore Wind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eTWh/yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003eMedium \u0026amp; Large Hydropower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003e25700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.42372881355932%\"\u003e\n \u003cp\u003eGeothermal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.932203389830505%\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 9: Estimated Fossil Fuel Reserves [17]\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.813559322033896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"41.186440677966104%\"\u003e\n \u003cp\u003eProven Reserves\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.813559322033896%\"\u003e\n \u003cp\u003eCoal (million tonnes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"41.186440677966104%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.813559322033896%\"\u003e\n \u003cp\u003eCrude Oil (billion barrels)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"41.186440677966104%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.813559322033896%\"\u003e\n \u003cp\u003eNatural Gas (trillion cubic metres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"41.186440677966104%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e1.8 Electricity Demand Projection\u003c/h2\u003e\n\u003cp\u003eFinal electricity demand in Taiwan was estimated at 849.79 PJ in 2016 and is forecasted to reach 917.63 PJ by 2030 and 901.37 PJ by 2050 in a Business as Usual (BAU) scenario according to the APEC Energy Supply and Demand Outlook 7th Edition [17]. Figure 4 below shows the final electricity demand projection.\u003c/p\u003e\n"},{"header":"2 Experimental Design, Materials, And Methods","content":"\u003cp\u003eData were primarily collected from the reports and websites of international organizations, including the International Renewable Energy Agency (IRENA), the Asia Pacific Economic Cooperation (APEC), the Economic Research Institute for ASEAN and East Asia (ERIA), the International Energy Agency (IEA), and the Intergovernmental Panel on Climate Change (IPCC). The data sources used are detailed in this section. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003ch2\u003e2.1 Electricity Supply System Data\u003c/h2\u003e\n\u003cp\u003eData on Taiwan\u0026apos;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 [23]. 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. Cost, efficiency 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-specific cost reduction trends from a very recent IRENA report focussing on Africa [9] were applied to the current Asia-specific cost estimates [7,8]. For offshore wind, the cost reduction trend was instead taken from a technology-specific IRENA report on the future of wind [25] 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].\u003c/p\u003e\n\u003cp\u003eCountry-specific 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-specific 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 fixed 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 a study by Singh and Kumar [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 refineries were sourced from the IEA Energy Technology Systems Analysis Programme (ETSAP) [16], which provides generic estimates of costs and performance parameters, while the refinery options modelled are based on the methods used in The Electricity Model Base for Africa [25].\u003c/p\u003e\n\u003ch2\u003e2.2 Fuel Data\u003c/h2\u003e\n\u003cp\u003eFuel 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-specific 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].\u003c/p\u003e\n\u003ch2\u003e2.3 Emissions Factors and Domestic Reserves\u003c/h2\u003e\n\u003cp\u003eEmissions factors were collected from the IPCC Emission Factor Database [20], which provides carbon emissions factors by fuel. The domestic wind resources were collected from an NREL dataset, which provides estimates of potential yearly generation by country [12]. Other renewable energy potentials were sourced from national studies [21,22]. Estimated domestic fossil fuel reserves were sourced from the APEC Energy Outlook 7th Edition [17], which provides estimates of reserves by country. Although Taiwan may have some domestic coal resources, an estimate could not be sourced since reserves have not been estimated since production stopped in 2001 [17].\u003c/p\u003e\n\u003ch2\u003e2.4 Electricity Demand Data\u003c/h2\u003e\n\u003cp\u003eThe final electricity demand projection is based on the BAU projection from the APEC Energy Outlook 7th Edition [17], which provides total demand estimates for every five years from 2015 to 2050, with demand assumed to change linearly between these data points. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"3 Ethics Statement","content":"\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"4 Credit Author Statement","content":"\u003cp\u003eLucy Allington: Data curation; Investigation; Methodology; Writing \u0026ndash; original draft; Visualisation. Carla Cannone: Data curation; Investigation; Software; Formal analysis; Visualisation. Ioannis Pappis: Data curation; Investigation; Validation; Writing - Review \u0026amp; Editing. Karla Cervantes Barron: Data Curation; Software; Visualisation. William Usher: Software; Supervision. Steve Pye: Supervision; Project Administration. Edward Brown: Funding Acquisition. Mark Howells: Conceptualisation; Methodology; Writing \u0026ndash; Review \u0026amp; Editing; Supervision. Miriam Zachau Walker: Software. Aniq Ahsan: Software. Flora Charbonnier: Software. Claire Halloran: Software. Stephanie Hirmer: Supervision; Writing - Review \u0026amp; Editing. Constantinos Taliotis: Conceptualisation; Writing - Review \u0026amp; Editing. Caroline Sundin: Conceptualisation; Writing - Review \u0026amp; Editing. Vignesh Sridharan: Conceptualisation. Eunice Ramos: Conceptualisation. Maarten Brinkerink: Data curation. Paul Deane: Data Curation. Gustavo Moura: Data Curation. Arnaud Rouget: Conceptualisation. Andrii Gritsevskyi: Conceptualisation. David Wogan: Conceptualisation. Edito Barcelona: Conceptualisation. Holger Rogner: Conceptualisation. Jennifer Cronin:\u0026nbsp;Writing - Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge data providers who helped make this, and future iterations possible, they include IEA, UNSTATS, APEC, IRENA, UCC, KTH, UFOP and others.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eAs well as support in kind provided by the employers of the authors of this note, we also acknowledge core funding from the Climate Compatible Growth Program (#CCG) of the UK\u0026apos;s Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government\u0026rsquo;s official policies.\u003c/p\u003e\n\u003ch2\u003eDeclaration of Competing Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCannone C. Towards evidence-based policymaking: energy modelling tools for sustainable development [Projecte Final de M\u0026agrave;ster Oficial]. 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Energy Policy. 2011 Oct 1;39(10):5850\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllington, L., Cannone, C., Pappis, I., Cervantes Barron, K., Usher, W., et al. (2021). CCG Starter Data Kit: Taiwan. (Version v1.0.0) [Data set]. Zenodo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.5281/zenodo.5139521\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllington L., Cannone C., Hooseinpoori P., Kell A., Taibi E., Fernandez C., Hawkes A., Howells M, 2021. Energy and Flexibility Modelling. Release Version 1.0 [online course]. Climate Compatible Growth Programme and the International Renewable Energy Agency. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.open.edu/openlearncreate/course/view.php?id=6817\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"U4RIA, Renewable energy, Cost-optimization, Taiwan, Energy policy, CCG, OSeMOSYS ","lastPublishedDoi":"10.21203/rs.3.rs-757733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-757733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnergy 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 Taiwan, 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\u0026ndash;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.\u003c/p\u003e","manuscriptTitle":"Selected ‘Starter Kit’ energy system modelling data for Taiwan (#CCG)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-07-28 17:53:40","doi":"10.21203/rs.3.rs-757733/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9526472b-7ea4-4712-a6ef-3ad22381e815","owner":[],"postedDate":"July 28th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6048505,"name":"Energy Engineering"}],"tags":[],"updatedAt":"2021-07-28T17:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2021-07-28 17:53:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-757733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-757733","identity":"rs-757733","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}