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Found 11 papers, 5 papers with code
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time
Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems.
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support
The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results.
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
Optimal Power Flow (OPF) is a fundamental problem in power systems.
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of disciplined dataset creation and benchmarking prohibits useful comparison among approaches in the literature.
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management
Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network.
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization
Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.
Learning-Accelerated ADMM for Distributed Optimal Power Flow
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e. g., milliseconds), bypassing solving an AC OPF altogether.
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part II: Case studies
Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance.
Optimization and Control Systems and Control
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part I: Methodologies
We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
