Abstract
Despite abundant renewable resources, Indonesia’s heavy reliance on coal for electricity generation results in high emissions and an electricity surplus, which could hinder the integration of renewable energy. To that end, this study involves a novel framework application to softlink OSeMOSYS and FlexTool, evaluating the potential of energy storage to enhance power system flexibility and facilitate a higher share of renewables in Indonesia’s future power sector. Six scenarios covering 2015 to 2070 were modelled using a soft-linking approach combining OSeMOSYS for capacity planning with FlexTool for operational analysis. These scenarios include Business as Usual, Nationally Determined Contributions, and Just Energy Transition Partnership, each modelled with and without energy storage. Results reveal that achieving Indonesia’s climate targets will require significant reliance on solar energy, which could lead to substantial curtailment and load losses. Incorporating energy storage could mitigate these issues, thus enabling a renewable energy share of 64.45% by 2060 while reducing total system costs and CO2 emissions. They also highlight that energy storage only becomes cost-effective when renewable energy shares are high, with hydrogen storage likely to surpass pumped hydro storage after 2050. Based on these findings, three key policy recommendations are proposed: implementing a moratorium on new coal-fired power plants, establishing a dedicated financing facility for energy storage, and developing a clear energy storage roadmap with specific targets.
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Title
Technoeconomic Data and Assumptions for Energy Systems Modelling (OSeMOSYS and FlexTool) in Indonesia
Description
The dataset includes technoeconomic data and assumptions for energy system modelling in Indonesia from 2015 to 2070. It encompasses capital costs, fixed costs, variable costs, power plant operational lifespans, technology efficiency, emission factors, capacity factors, demand, residual capacity, electricity production, and renewable energy supply and potential, all of which are adapted for OSeMOSYS modelling. Additionally, it provides key parameter values for FlexTool modelling.
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