{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/163575"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/163575","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Design of Future Energy Infrastructure: Understanding trade-offs between Renewable Capacity, Storage and Transmission Networks for Low-Carbon Landscape","abstract":"In 2021, the United States committed to achieving net-zero greenhouse gas emissions by 2050, requiring a fundamental transformation of its energy infrastructure. This thesis develops a nationwide optimization model to minimize capital expenditures and understand the trade-off between renewable capacity, storage, and transmission networks. The results show that the least-cost configuration, achieved when nuclear and battery capital costs fall by 50%, requires approximately $3.25 trillion in new investment - a 37% reduction relative to the baseline scenario. Comparative scenario analysis reveals a marked shift toward centralized storage when nuclear costs decline, which improves reliability and reduces contingency requirements - mirroring inventory pooling dynamics in supply chains. Concurrently, wind capacity additions fall sharply, with each 10% reduction in nuclear cost halving the predicted wind capacity addition. Transmission infrastructure evolves accordingly: 765 kV lines decline as nuclear becomes more decentralized, while 230 kV lines expand modestly to manage increased intermittency. By quantifying trade-offs across technologies and identifying system tipping points, this work offers a framework for policymakers and long-horizon investors.","abstract_html":"In 2021, the United States committed to achieving net-zero greenhouse gas emissions by 2050, requiring a fundamental transformation of its energy infrastructure. This thesis develops a nationwide optimization model to minimize capital expenditures and understand the trade-off between renewable capacity, storage, and transmission networks. The results show that the least-cost configuration, achieved when nuclear and battery capital costs fall by 50%, requires approximately $3.25 trillion in new investment - a 37% reduction relative to the baseline scenario. Comparative scenario analysis reveals a marked shift toward centralized storage when nuclear costs decline, which improves reliability and reduces contingency requirements - mirroring inventory pooling dynamics in supply chains. Concurrently, wind capacity additions fall sharply, with each 10% reduction in nuclear cost halving the predicted wind capacity addition. Transmission infrastructure evolves accordingly: 765 kV lines decline as nuclear becomes more decentralized, while 230 kV lines expand modestly to manage increased intermittency. By quantifying trade-offs across technologies and identifying system tipping points, this work offers a framework for policymakers and long-horizon investors.","abstract_has_math":false,"creators":["Bhupathi, Hari Raghavendran"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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This thesis develops a nationwide optimization model to minimize capital expenditures and understand the trade-off between renewable capacity, storage, and transmission networks. The results show that the least-cost configuration, achieved when nuclear and battery capital costs fall by 50%, requires approximately $3.25 trillion in new investment - a 37% reduction relative to the baseline scenario. Comparative scenario analysis reveals a marked shift toward centralized storage when nuclear costs decline, which improves reliability and reduces contingency requirements - mirroring inventory pooling dynamics in supply chains. Concurrently, wind capacity additions fall sharply, with each 10% reduction in nuclear cost halving the predicted wind capacity addition. Transmission infrastructure evolves accordingly: 765 kV lines decline as nuclear becomes more decentralized, while 230 kV lines expand modestly to manage increased intermittency. By quantifying trade-offs across technologies and identifying system tipping points, this work offers a framework for policymakers and long-horizon investors."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Design of Future Energy Infrastructure: Understanding trade-offs between Renewable Capacity, Storage and Transmission Networks for Low-Carbon Landscape"]}]}],"canonical_facts":{"dc:contributor.advisor":["Caplice, Chris"],"dc:contributor.department":["Massachusetts Institute of Technology. Supply Chain Management Program"],"dc:creator":["Bhupathi, Hari Raghavendran"],"dc:date.accessioned":["2025-11-05T19:35:42Z"],"dc:date.available":["2025-11-05T19:35:42Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["In 2021, the United States committed to achieving net-zero greenhouse gas emissions by 2050, requiring a fundamental transformation of its energy infrastructure. This thesis develops a nationwide optimization model to minimize capital expenditures and understand the trade-off between renewable capacity, storage, and transmission networks. The results show that the least-cost configuration, achieved when nuclear and battery capital costs fall by 50%, requires approximately $3.25 trillion in new investment - a 37% reduction relative to the baseline scenario. Comparative scenario analysis reveals a marked shift toward centralized storage when nuclear costs decline, which improves reliability and reduces contingency requirements - mirroring inventory pooling dynamics in supply chains. Concurrently, wind capacity additions fall sharply, with each 10% reduction in nuclear cost halving the predicted wind capacity addition. Transmission infrastructure evolves accordingly: 765 kV lines decline as nuclear becomes more decentralized, while 230 kV lines expand modestly to manage increased intermittency. 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