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Sensitivity Analysis in Structured Optimization Problems Methods and Applications to Power Systems Models
Sensitivity Analysis in Structured Optimization Problems Methods and Applications to Power Systems Models
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151403
- ISBN
- 9798382233451
- DDC
- 620
- 서명/저자
- Sensitivity Analysis in Structured Optimization Problems Methods and Applications to Power Systems Models
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 93 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Marco Pavone.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약This work presents developments in differentiable optimization, with applications to the computation of marginal emissions in power system models. First, we discuss how recent results in differentiable optimization can be used readily to compute any emissions sensitivity metric (including marginal emissions) in optimization-based models. Second, we develop a general decentralized scheme for differentiation of graph-structured optimization problems. The methodology is efficient and can be made fully distributed, with convergence guarantees. Finally, we come full circle and illustrate the benefits of the decentralized framework in the computation of marginal emission factors. Using historical data, we demonstrate how the proposed approach allows for efficient computation of marginal emissions in large network models.
- 일반주제명
- Engineering
- 일반주제명
- Electrical engineering
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382233451
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aFuentes Valenzuela, Lucas.
■24510▼aSensitivity Analysis in Structured Optimization Problems Methods and Applications to Power Systems Models
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a93 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Marco Pavone.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aThis work presents developments in differentiable optimization, with applications to the computation of marginal emissions in power system models. First, we discuss how recent results in differentiable optimization can be used readily to compute any emissions sensitivity metric (including marginal emissions) in optimization-based models. Second, we develop a general decentralized scheme for differentiation of graph-structured optimization problems. The methodology is efficient and can be made fully distributed, with convergence guarantees. Finally, we come full circle and illustrate the benefits of the decentralized framework in the computation of marginal emission factors. Using historical data, we demonstrate how the proposed approach allows for efficient computation of marginal emissions in large network models.
■590 ▼aSchool code: 0212.
■650 4▼aEngineering
■650 4▼aElectrical engineering
■653 ▼aStructured optimization problems
■653 ▼aPower system models
■653 ▼aMarginal emissions
■653 ▼aDifferentiable optimization
■690 ▼a0544
■690 ▼a0537
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161488▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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