서브메뉴
검색
상세정보
Resilience-Focused Stochastic Programming for Optimizing Power System Investments.
Resilience-Focused Stochastic Programming for Optimizing Power System Investments.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Resilience-Focused Stochastic Programming for Optimizing Power System Investments.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 196 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 일반주기
- Advisor: Luedtke, James.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
- 요약 등 주기
- 요약Optimizing power system investments is a complex network optimization challenge, involving a vast and growing system governed by non-convex physics and subject to significant uncertainty. The diversity of generation technologies and configurations introduces many binary decisions, requiring model simplifications to ensure tractability. As the grid evolves with new technologies and shifting demand patterns, past assumptions may no longer hold, necessitating new approaches to investment planning that enhance resilience and efficiency.This work develops methods for optimizing long- and medium-term grid investments under uncertainty. We first address long-term transmission-level capacity expansion, balancing cost with resilience to extreme events. We propose a conditional sampling technique to reduce the number of scenarios needed to capture high-impact, low-frequency risks, incorporating it into a bi-objective optimization framework. We also introduce a statistical model for generating spatially correlated extreme temperature scenarios. A large-scale case study shows that conditional sampling helps effectively identify cost-risk tradeoffs and that modeling temperature dependence and spatial correlation significantly affects investment decisions.At the distribution level, we propose a model for medium-term investment in distributed energy resources and control devices to enhance reliability during outages, and we develop a scalable solution using network flow approximations and Benders decomposition. The model balances reliability improvements during outages with normal-operation cost savings from resources like distributed solar. We find that the network flow approximation offers effective guidance for planning decisions and that small adjustments to operational policies can significantly affect the accuracy of the approximation and the efficiency of computation.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
008260219s2025 us ||||||||||||||c||eng d■001000017359342
■00520260202105105
■006m o d
■007cr#unu||||||||
■020 ▼a9798291548004
■035 ▼a(MiAaPQ)AAI32236536
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aRossmann, Ramsey.
■24510▼aResilience-Focused Stochastic Programming for Optimizing Power System Investments.
■260 ▼a[S.l.]▼bThe University of Wisconsin - Madison. ▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a196 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Luedtke, James.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
■520 ▼aOptimizing power system investments is a complex network optimization challenge, involving a vast and growing system governed by non-convex physics and subject to significant uncertainty. The diversity of generation technologies and configurations introduces many binary decisions, requiring model simplifications to ensure tractability. As the grid evolves with new technologies and shifting demand patterns, past assumptions may no longer hold, necessitating new approaches to investment planning that enhance resilience and efficiency.This work develops methods for optimizing long- and medium-term grid investments under uncertainty. We first address long-term transmission-level capacity expansion, balancing cost with resilience to extreme events. We propose a conditional sampling technique to reduce the number of scenarios needed to capture high-impact, low-frequency risks, incorporating it into a bi-objective optimization framework. We also introduce a statistical model for generating spatially correlated extreme temperature scenarios. A large-scale case study shows that conditional sampling helps effectively identify cost-risk tradeoffs and that modeling temperature dependence and spatial correlation significantly affects investment decisions.At the distribution level, we propose a model for medium-term investment in distributed energy resources and control devices to enhance reliability during outages, and we develop a scalable solution using network flow approximations and Benders decomposition. The model balances reliability improvements during outages with normal-operation cost savings from resources like distributed solar. We find that the network flow approximation offers effective guidance for planning decisions and that small adjustments to operational policies can significantly affect the accuracy of the approximation and the efficiency of computation.
■590 ▼aSchool code: 0262.
■650 4▼aElectrical engineering.
■650 4▼aComputer engineering.
■650 4▼aEngineering.
■650 4▼aSystems science.
■653 ▼aExpansion planning
■653 ▼aInteger programming
■653 ▼aPower systems
■653 ▼aStochastic programming
■690 ▼a0796
■690 ▼a0544
■690 ▼a0464
■690 ▼a0537
■690 ▼a0790
■71020▼aThe University of Wisconsin - Madison▼bIndustrial Engineering.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359342▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
