• Corpus ID: 239050153

Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment

@article{Kody2021ModelingTA,
  title={Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment},
  author={Alyssa Kody and Samuel C. Chevalier and Spyros Chatzivasileiadis and Daniel K. Molzahn},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11269}
}
Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using Neural Networks (NNs). These NNs can be exactly transformed into Mixed Integer Linear Programs (MILPs) and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear… 

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References

SHOWING 1-10 OF 29 REFERENCES
The Unit Commitment Problem With AC Optimal Power Flow Constraints
TLDR
A mathematical programming-based approach to optimize the unit commitment problem with alternating current optimal power flow (ACOPF) network constraints, which can be extended to solve larger scale power systems as well as include security constraints or uncertainty through decomposition techniques.
Fast power system analysis via implicit linearization of the power flow manifold
  • S. Bolognani, F. Dörfler
  • Mathematics, Computer Science
    2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2015
TLDR
This paper derives the best linear approximant of such a relation around a generic solution of the power flow equations as an implicit algebraic relation between nodal voltages and nodal power injections.
Global Solution Strategies for the Network-Constrained Unit Commitment Problem With AC Transmission Constraints
We propose a novel global solution algorithm for the network-constrained unit commitment problem that incorporates a nonlinear alternating current (ac) model of the transmission network, which is a
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
TLDR
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example, and shows how to systematically reduce the worst- case guarantees by training on a larger input domain than the domain they are evaluated on.
PowerModels.J1: An Open-Source Framework for Exploring Power Flow Formulations
TLDR
This work proposes PowerModels, an open-source platform for comparing power flow formulations, and provides a brief introduction to the design, validates its implementation, and demonstrates its effectiveness with a proof-of-concept study analyzing five different formulations of the Optimal Power Flow problem.
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
TLDR
This IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPf networks for benchmarking AC-opF algorithms and a motivating study demonstrates some limitations of the established network datasets in the context of benchmarking ASF algorithms.
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs
TLDR
This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks, and demonstrates the approach for power system operation considering N-1 security and small-signal stability.
Sequential Relaxation of Unit Commitment with AC Transmission Constraints
TLDR
This paper develops a second-order cone programming (SOCP) relaxation for AC unit commitment and incorporates penalty terms into the objective of the proposed SOCP relaxation to ensure that the resulting solutions are feasible for the original non-convex problem.
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