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.
Equivalent Circuit Programming for Power Flow Analysis and Optimization
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abstract
The utility of domain-specific knowledge for modeling, simulation, and optimization has been demonstrated for various research problem domains, including power systems. The concept of Equivalent Circuit Programming was previously developed and facilitated for robust, efficient, and scalable solution of network simulation and optimization problems. This paper extends the theoretical foundation of Equivalent Circuit Programming to enable the fusion of optimization theory and algorithms with the numerical methods that utilize the domain-specific knowledge of power flow models. The generality, scalability, and numerical robustness of the resulting framework are demonstrated on realistic AC power flow (ACPF) models of up to 70k buses with proper enforcement of industry-required operational and security constraints.
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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.