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The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
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In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods. However, it is often difficult to directly compare these studies due to subtle differences in the AC-OPF problem formulation as well as the network, generation, and loading data that are used for evaluation. To help address these challenges, this IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPF networks for benchmarking AC-OPF algorithms. A motivating study demonstrates some limitations of the established network datasets in the context of benchmarking AC-OPF algorithms and a validation study demonstrates the efficacy of using the PGLib-OPF networks for this purpose. In the interest of scientific discourse and future additions, the PGLib-OPF benchmark library is open-access and all the of network data is provided under a creative commons license.
Forward citations
Cited by 12 Pith papers
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The Limits of Quantum Computers for Power Flow
Balanced separators and corridors in transmission grids force the DC susceptance matrix condition number to grow polynomially, ruling out end-to-end quantum advantage at any readout level.
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A new asymmetric regret cost function for scenario reduction provably picks the single best scenario and, with a hybrid pre-selection, matches its accuracy at a fraction of the compute.
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De-risking solutions to optimization problems
A softmax-based cutting-plane method de-risks solutions of generic optimization problems by reducing an impact metric with limited cost increase, or certifies impossibility.
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Activate the Dual Cones: A Tight Reformulation of Conic ACOPF Constraints
The dual rotated second-order cone constraints of the Jabr ACOPF relaxation are always active at optimality, which lets the paper replace them with equality constraints and produce a certified lower bound.
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Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets
A physics-guided diffusion model produces synthetic power flow samples that are more AC-feasible and slightly closer to ground truth than unconstrained diffusion on IEEE 5, 24, and 118 bus systems.
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PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
PGLearn provides a large open-source dataset collection and toolkit with AC, DC, and SOC-OPF primal and dual solutions, time-series data for large grids, and benchmarking tools for ML-based OPF methods.
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Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow
A self-supervised neural network with a dual-completion layer produces valid, fast lower bounds for the SDP relaxation of AC optimal power flow, outperforming SOC-based proxies on some benchmarks.
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MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
MxGPS jointly trains state-estimation and power-flow branches over a shared encoder and reports more stable zero-shot behavior on unseen grids, at the price of higher in-distribution error.
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A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Sampling the total active power load instead of individual loads produces more diverse AC-OPF datasets, and a slack-variable formulation lets the generator scale to 4,661-bus grids.
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Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
The paper proposes a dispatch-aware neural network that learns transmission switching decisions by minimizing the generation cost of a differentiable DC-OPF layer, yielding fast heuristic solutions to DC-OTS.
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Exploring the potential of ChatGPT for feedback and evaluation in experimental physics
ChatGPT is more reliable for formal structure of experimental-physics lab reports than for technical accuracy or interpretation of experimental data, so instructor oversight remains necessary.
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Scalable Global Optimization for AC-OPF via Quadratic Convex Relaxation and Branch-and-Bound
A fixed-depth branch-and-bound algorithm using QC relaxation lower bounds narrows the optimality gap on small PGLib cases, without proof of global optimality.
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