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Computational Methods for Advancing a Decarbonized, Equitable, and Resilient Energy Transition
Computational Methods for Advancing a Decarbonized, Equitable, and Resilient Energy Transition
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153058
- ISBN
- 9798346380504
- DDC
- 621
- 서명/저자
- Computational Methods for Advancing a Decarbonized, Equitable, and Resilient Energy Transition
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 159 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
- 주기사항
- Advisor: Majumdar, Arunava.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약The electricity grid is an engineering marvel-one of humanity's greatest technological feats of the 20th century. In the race to combat climate change, the electricity grid is experiencing a renaissance, driven by rapid changes within the transportation, industrial, and electricity sectors, etc. that seek to reduce their carbon footprint. Historically, electricity has flowed in one direction-from power plants to customers. However, the proliferation of distributed energy resources is changing this; today electricity in the grid can flow in many directions-a great departure from its historical context. This lends a profound opportunity to harness our clean, natural, and intermittent endowments (solar, wind, etc.) to keep the lights on while the climate is changing, and this is no trivial feat.This dissertation traverses the landscape of the energy transition using computational methods as a vehicle, with emphasis on the electricity grid and the energy transition. This body of work lies at the confluence of the climate, grid, and transportation, and is composed of three connected thrusts. The first thrust proposes computationally efficient generative models for stochastic weather generation, which is integral during the operational planning of the electricity grid. The second thrust elucidates the importance of high fidelity models and an integrated system for operational planning within the context of electric vehicle infrastructure support. In this thrust, we first propose a generative machine learning model for capturing stochastic loads. Then, we develop a multifidelity and multi-timescale platform for designing and integrating new technologies, such as electric vehicle supply equipment into the grid, with economic and grid-feasibility considerations. With the detailed design, we show that the battery model fidelity has enormous economic ramifications during operational planning. In the third thrust, this dissertation assesses the role of markets for an equitable grid. We develop new methods grounded in optimization theory to propose a new approach to designing power systems equitably. In this thrust, we discuss an equitable dynamic pricing scheme that can significantly improve the operation of the distribution grid, outperforming the current pricing method. Furthermore, we qualify equity through the lens of energy affordability and propose methods for allocating or upgrading non-wires distribution grid assets, such as solar systems, batteries, and transformers while seeking to achieve equity. Finally, we discuss some policy interventions that can lead to the desired load flexibility needed for customers to achieve equity within a distribution grid.This dissertation encompasses computational methods based on three foundational levers- climate, power systems, and markets-that we believe society should rethink and reinvent to ensure a just and climate-resilient future energy system. We hope that this work can provide an inflection or starting point for researchers, engineers, startups, utilities, electricity market makers, and anyone within the energy landscape to attain a carbon-free, climate-resilient, and just electricity grid.
- 일반주제명
- Energy
- 일반주제명
- Marginal pricing
- 일반주제명
- Finance
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-05A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153058
■006m o d
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■020 ▼a9798346380504
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■035 ▼a(MiAaPQ)Stanforddt985yg9346
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aBalogun, Emmanuel O.
■24510▼aComputational Methods for Advancing a Decarbonized, Equitable, and Resilient Energy Transition
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a159 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-05, Section: A.
■500 ▼aAdvisor: Majumdar, Arunava.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aThe electricity grid is an engineering marvel-one of humanity's greatest technological feats of the 20th century. In the race to combat climate change, the electricity grid is experiencing a renaissance, driven by rapid changes within the transportation, industrial, and electricity sectors, etc. that seek to reduce their carbon footprint. Historically, electricity has flowed in one direction-from power plants to customers. However, the proliferation of distributed energy resources is changing this; today electricity in the grid can flow in many directions-a great departure from its historical context. This lends a profound opportunity to harness our clean, natural, and intermittent endowments (solar, wind, etc.) to keep the lights on while the climate is changing, and this is no trivial feat.This dissertation traverses the landscape of the energy transition using computational methods as a vehicle, with emphasis on the electricity grid and the energy transition. This body of work lies at the confluence of the climate, grid, and transportation, and is composed of three connected thrusts. The first thrust proposes computationally efficient generative models for stochastic weather generation, which is integral during the operational planning of the electricity grid. The second thrust elucidates the importance of high fidelity models and an integrated system for operational planning within the context of electric vehicle infrastructure support. In this thrust, we first propose a generative machine learning model for capturing stochastic loads. Then, we develop a multifidelity and multi-timescale platform for designing and integrating new technologies, such as electric vehicle supply equipment into the grid, with economic and grid-feasibility considerations. With the detailed design, we show that the battery model fidelity has enormous economic ramifications during operational planning. In the third thrust, this dissertation assesses the role of markets for an equitable grid. We develop new methods grounded in optimization theory to propose a new approach to designing power systems equitably. In this thrust, we discuss an equitable dynamic pricing scheme that can significantly improve the operation of the distribution grid, outperforming the current pricing method. Furthermore, we qualify equity through the lens of energy affordability and propose methods for allocating or upgrading non-wires distribution grid assets, such as solar systems, batteries, and transformers while seeking to achieve equity. Finally, we discuss some policy interventions that can lead to the desired load flexibility needed for customers to achieve equity within a distribution grid.This dissertation encompasses computational methods based on three foundational levers- climate, power systems, and markets-that we believe society should rethink and reinvent to ensure a just and climate-resilient future energy system. We hope that this work can provide an inflection or starting point for researchers, engineers, startups, utilities, electricity market makers, and anyone within the energy landscape to attain a carbon-free, climate-resilient, and just electricity grid.
■590 ▼aSchool code: 0212.
■650 4▼aEnergy
■650 4▼aMarginal pricing
■650 4▼aFinance
■690 ▼a0791
■690 ▼a0508
■690 ▼a0454
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-05A.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164883▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
