title: Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
authors: Wenqian Dong Zhen Xie Gokcen Kestor Dong Li
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  : Using Neural Network to Accelerate AC-OPF Power Grid Simulation
      Wenqian Dong
  UC Merced 
  
  California, USA
  
  wdong5@ucmerced.edu 
  Zhen Xie
  UC Merced
  
  California, USA 
  
  zxie10@ucmerced.edu
  Gokcen Kestor
  Pacific Northwest National Laboratory 
  
  Washington, USA 
  
  gokcen.kestor@pnnl.gov
  Dong Li
  UC Merced
  
  California, USA
  
  dli35@ucmerced.edu
  
      Aug. 2020
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  fancy
  
  
  
  
  SC20, November 15-20, 2020, Atlanta, GA, USA
  978-1-5386-8384-2/XX/$31.00 2020 IEEE
  
  
  
   
  
  
  
  
  
  In this work we address the problem of accelerating complex power-grid simulation through machine learning (ML). Specifically, we develop a framework, , which generates multitask-learning (MTL) neural network (NN) models to predict the initial values of variables critical to the problem convergence. 
  MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables.
  We show that, to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model.  then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. brings 2.60× speedup on average (up to 3.28×) computed over 10,000 problems, without losing solution optimality.
  
  
  
  
  
  
  
  
  
   ontent/Introduction
   ontent/ProblemFormulation
    ontent/RelatedWork
   ontent/Overview 
   ontent/SensetiveStudy
   
   ontent/MultitaskLearning
  
   ontent/PhysicalLearning
   
   ontent/Evaluation
    ontent/Discussion
   ontent/Conclusions
  
  
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