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

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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.