Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
This work addresses uncertainty management in power systems, offering a more efficient method for grid operators, though it is incremental as it builds on existing chance-constrained frameworks.
The paper tackles the challenge of managing uncertainties in power systems by developing a novel chance-constrained optimization approach for DC Optimal Power Flow, which reduces dimensionality by modeling aggregate system and line errors instead of high-dimensional nodal injections, leading to significant improvements in statistical accuracy and optimization performance compared to existing methods.
Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (DC-OPF) model, developing a novel approach to uncertainty modeling and estimation. Current methods typically tackle these problems by first modeling random nodal injections using high-dimensional statistical distributions that scale with the number of buses, followed by deriving deterministic reformulations of the probabilistic constraints. We propose an alternative methodology that exploits the constraint structure to inform the uncertainties to be estimated, enabling significant dimensionality reduction. Rather than learning joint distributions of net-load forecast errors across units, we instead directly model the one-dimensional aggregate system forecast error and two-dimensional line errors weighted by power transfer distribution factors. We evaluate our approach under both Gaussian and non-Gaussian distributions on synthetic and real-world datasets, demonstrating significant improvements in statistical accuracy and optimization performance compared to existing methods.