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

美国的输电系统大多建于20世纪60年代和70年代，为全国各地的消费者提供了可靠的电力传输基础设施。然而，对可再生能源、电力电子和燃料成本变化的推动正在从根本上改变能源格局。基于电网的高保真模型和可再生能源的准确预测模型，在全国范围内对输电规划进行全面研究，将产生显著的成本和环境效益。该项目的目标是实现传输规划系统在保真度、可扩展性和性能方面的逐步改变。这项研究将对科学以及广泛的社会和教育产生影响。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，研究结果将通过教育倡议传播。将通过参与密歇根大学的各种倡议来实施若干教育计划，包括促进K-12教育以及妇女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，这些模型捕获了复杂的时空相关性，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具有专业知识。提出了基于各种风险和鲁棒性措施的输电规划多阶段随机规划，并采用优先级方法来表达规划者在不确定性揭示时的偏好。期望通过使用凸松弛、大邻域搜索和分解算法的并行实现来实现高计算性能。提议的算法将在欧洲最大的输电运营商提供的真实测试用例中进行评估，测试用例范围从2000到20,000辆公交车，以及根据这些测试用例改编的适合美国实际情况的合成版本。

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.