High-Fidelity, High-Performance Multi-Stage Transmission Planning with Spatio-Temporal Uncertainty Models

利用时空不确定性模型进行高保真、高性能多级传输规划

• 批准号: 1912244
• 负责人: Pascal Van Hentenryck
• 金额: $ 30.13万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912244&HistoricalAwards=false
• 关键词: Fidelity; Performance; Multi; Stage; Transmission

The transmission system of the United States, mostly built in the 1960s and 1970s, provides the backbone infrastructure to deliver electricity with great reliability to consumers all over the country. However, the drive towards renewable energy, power electronics, and changes in fuel costs are fundamentally altering the energy landscape. Significant cost and environmental benefits would result from a holistic study of transmission planning at the national scale, based on a high-fidelity modeling of the grid and accurate forecasting models for renewable energy. The goal of this project is to realize a step change in the fidelity, scalability, and performance of transmission planning systems. The research will have scientific as well as broad societal and educational impact. The project will also pioneer methodologies for solving important problems in energy optimization and management, and generate both short-term and long-term technical impacts. Moreover, the research results will be disseminated through education initiatives. Several education plans, including promoting K-12 education and participation of female and underrepresented minority groups in science and engineering, will be undertaken through involvement in various initiatives at the University of Michigan. This research proposes a new generation of transmission planning systems to amplify the benefits of renewable energy, while addressing the challenges created by increasing stochasticity and power electronics. The project relies on high-fidelity models of the grid and novel, hierarchical predictive models for renewable energy and substation loads that capture complex spatio-temporal correlations that are critical in obtaining realistic characterizations of uncertainties. The project brings together four PI and Co-PIs with their expertise in power system optimization, uncertainty quantification, algorithm design, and large-scale distributed computing. It proposes multi-stage stochastic programs over various risk and robustness measures for transmission planning and adopts a prioritization methodology to express planner preferences as the uncertainties are being revealed. It is expected to achieve high computational performance through the use of convex relaxations, large neighborhood search, and parallel implementations of decomposition algorithms. The proposed algorithms will be evaluated on real test cases offered by the largest transmission operator in Europe, ranging from 2,000 to 20,000 buses, as well as synthetic versions from these test cases that are adapted to the realities of the United States.

美国的输电系统大部分建于1960年代和1970年代，提供了骨干基础设施，以高度可靠的方式向全国各地的消费者输送电力。然而，可再生能源、电力电子技术的发展以及燃料成本的变化正在从根本上改变能源格局。基于对电网的高保真建模和可再生能源的准确预测模型，对全国范围的输电规划进行全面研究，将产生重大的成本和环境效益。该项目的目标是实现传输规划系统的保真度，可扩展性和性能的一步变化。这项研究将产生科学以及广泛的社会和教育影响。该项目还将开创解决能源优化和管理方面重要问题的方法，并产生短期和长期的技术影响。此外，将通过教育举措传播研究成果。将通过参与密歇根大学的各种举措，实施若干教育计划，包括促进K-12教育以及女性和代表性不足的少数群体参与科学和工程。这项研究提出了新一代的输电规划系统，以扩大可再生能源的好处，同时解决日益增加的随机性和电力电子所带来的挑战。该项目依赖于电网的高保真模型和可再生能源和变电站负荷的新型分层预测模型，这些模型捕捉复杂的时空相关性，这对于获得不确定性的现实特征至关重要。该项目汇集了四个PI和Co-PI，他们在电力系统优化，不确定性量化，算法设计和大规模分布式计算方面的专业知识。它提出了多阶段的随机规划在各种风险和鲁棒性措施的传输规划，并采用了优先级的方法来表达规划师的偏好，因为不确定性正在被揭示。预计通过使用凸松弛、大邻域搜索和并行实现分解算法来实现高计算性能。所提出的算法将在欧洲最大的传输运营商提供的真实的测试案例上进行评估，范围从2,000到20,000辆巴士，以及适应美国现实的这些测试案例的合成版本。

项目成果

An exact and scalable problem decomposition for security-constrained optimal power flow

安全约束最优潮流的精确且可扩展的问题分解

• DOI: 10.1016/j.epsr.2020.106677
• 发表时间: 2021
• 期刊: Electric Power Systems Research
• 影响因子: 3.9
• 作者: Velloso, Alexandre;Van Hentenryck, Pascal;Johnson, Emma S.
• 通讯作者: Johnson, Emma S.

On the long-term density prediction of peak electricity load with demand side management in buildings

• DOI: 10.1016/j.enbuild.2020.110450
• 发表时间: 2020-09
• 期刊: Energy and Buildings
• 影响因子: 6.7
• 作者: Youngchan Jang;E. Byon;E. Jahani;Kristen S. Cetin

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

• DOI: 10.1609/aaai.v34i01.5403
• 发表时间: 2019-09
• 期刊: ArXiv
• 作者: Ferdinando Fioretto;Terrence W.K. Mak;Pascal Van Hentenryck

Probabilistic Characterization of Wind Diurnal Variability for Wind Resource Assessment

风资源评估中风日变化的概率表征

• DOI: 10.1109/tste.2020.2965444
• 发表时间: 2020
• 期刊: IEEE Transactions on Sustainable Energy
• 影响因子: 8.8
• 作者: Jang, Youngchan;Byon, Eunshin
• 通讯作者: Byon, Eunshin

Look-ahead decision making for renewable energy: A dynamic “predict and store” approach

• DOI: 10.1016/j.apenergy.2021.117068
• 发表时间: 2021-08
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Jingxing Wang;Seokhyun Chung;Abdullah AlShelahi;R. Kontar;E. Byon;R. Saigal