2 research outputs found
Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
The optimal power flow (OPF) problem is one of the most important
optimization problems for the operation of the power grid. It calculates the
optimum scheduling of the committed generation units. In this paper, we develop
a neural network approach to the problem of accelerating the current optimal
power flow (AC-OPF) by generating an intelligent initial solution. The high
quality of the initial solution and guidance of other outputs generated by the
neural network enables faster convergence to the solution without losing
optimality of final solution as computed by traditional methods. Smart-PGSim
generates a novel multitask-learning neural network model to accelerate the
AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the
simulation on the neural network automatically. Smart-PGSim brings an average
of 49.2% performance improvement (up to 91%), computed over 10,000 problem
simulations, with respect to the original AC-OPF implementation, without losing
the optimality of the final solution
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
There is a growing consensus that solutions to complex science and
engineering problems require novel methodologies that are able to integrate
traditional physics-based modeling approaches with state-of-the-art machine
learning (ML) techniques. This paper provides a structured overview of such
techniques. Application-centric objective areas for which these approaches have
been applied are summarized, and then classes of methodologies used to
construct physics-guided ML models and hybrid physics-ML frameworks are
described. We then provide a taxonomy of these existing techniques, which
uncovers knowledge gaps and potential crossovers of methods between disciplines
that can serve as ideas for future research.Comment: 35 pages, 4 figures, submitted to ACM Computing Survey