A review paper that categorizes large-scale power grid optimization methods and reports that physics-based solvers dominate while physics-constrained machine learning is emerging.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
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abstract
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus brings a lot of uncertainty to power grid operations. This fact makes the conventional model-based CC-OPF problem non-convex and computationally complex to solve. The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy. The proposed approach and developed software can help system operators to effectively perform ED optimization in the presence of large uncertainties in the power grid.
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Large-scale Grid Optimization: The Workhorse of Future Grid Computations
A review paper that categorizes large-scale power grid optimization methods and reports that physics-based solvers dominate while physics-constrained machine learning is emerging.