Incorporating grid development in capacity expansion optimisation - a case study for Indonesia

被引:0
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作者
Yuwono, Bintang [1 ,2 ]
Kranzl, Lukas [1 ]
Haas, Reinhard [1 ]
Dewi, Retno Gumilang [3 ]
Siagian, Ucok Welo Risma [3 ]
Kraxner, Florian [2 ]
Yowargana, Ping [2 ]
机构
[1] Energy Economics Group (EEG), Institute of Energy Systems and Electrical Drive, Technischen Universität Wien (TU Wien), Austria
[2] Biodiversity and Natural Resources Program (BNR), International Institute for Applied Systems Analysis (IIASA)
[3] Center for Research on Energy Policy (CREP), Institut Teknologi Bandung (ITB), Indonesia
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D O I
10.1016/j.apenergy.2024.124837
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摘要
Capacity expansion optimisation is a widely used techno-economic analysis particularly on topics related to climate change mitigation and renewable energy transition. Using optimisation models to investigate capacity expansion in regions that potentially require significant grid infrastructure development requires incorporation of grid expansion problem within the optimisation. This study presents the development of SELARU, a spatially explicit optimisation model that incorporates the economies of scale of grid expansion using contextualized geographical feature to form the model's high-resolution spatial units. The model is used to investigate the case study of Indonesia using various spatial treatments to demonstrate the impact of detailed spatial depiction of grid expansion. Results reveal significant difference in renewable energy deployment trajectory (up to 2272 % increase in new generation capacity) between high-resolution spatial depiction of grid expansion vis-à-vis non spatially explicit energy system optimisation. Due to its high-resolution, SELARU also generates detailed information on the geographical extent of grid expansion requirement, which provides more realistic insights on governance challenges of renewable energy transition. Careful consideration of spatial representation is crucial when optimisation model is used to evaluate scenarios that concern technology selection such as renewable energy deployment or climate change mitigation. © 2024 The Authors
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