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- W4366999483 abstract "This paper presents an algorithm for restoring AC power flow feasibility from solutions to simplified optimal power flow (OPF) problems, including convex relaxations, power flow approximations, and machine learning (ML) models. The proposed algorithm employs a state estimation-based post-processing technique in which voltage phasors, power injections, and line flows from solutions to relaxed, approximated, or ML-based OPF problems are treated similarly to noisy measurements in a state estimation algorithm. The algorithm leverages information from various quantities to obtain feasible voltage phasors and power injections that satisfy the AC power flow equations. Weight and bias parameters are computed offline using an adaptive stochastic gradient descent method. These parameters inform the online computations of the state estimation-based algorithm to recover feasible solutions. Furthermore, the proposed algorithm can simultaneously utilize combined solutions from different relaxations, approximations, and ML models to enhance performance. Case studies demonstrate the effectiveness and scalability of the proposed algorithm, with numerical results showing several orders of magnitude improvement over traditional methods for restoring AC power flow feasible solutions." @default.
- W4366999483 created "2023-04-27" @default.
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- W4366999483 date "2023-04-22" @default.
- W4366999483 modified "2023-10-18" @default.
- W4366999483 title "AC Power Flow Feasibility Restoration via a State Estimation-Based Post-Processing Algorithm" @default.
- W4366999483 doi "https://doi.org/10.48550/arxiv.2304.11418" @default.
- W4366999483 hasPublicationYear "2023" @default.
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