Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids

arXiv (Cornell University)

Abstract

Power flow (PF) calculations are fundamental to power system analysis to ensure stable and reliable grid operation. The Newton-Raphson (NR) method is commonly used for PF analysis due to its rapid convergence when initialized properly. However, as power grids operate closer to their capacity limits, ill-conditioned cases and convergence issues pose significant challenges. This work, therefore, addresses these challenges by proposing strategies to improve NR initialization, hence minimizing iterations and avoiding divergence. We explore three approaches: (i) an analytical method that estimates the basin of attraction using mathematical bounds on voltages, (ii) Two data-driven models leveraging supervised learning or physics-informed neural networks (PINNs) to predict optimal initial guesses, and (iii) a reinforcement learning (RL) approach that incrementally adjusts voltages to accelerate convergence. These methods are tested on benchmark systems. This research is particularly relevant for modern power systems, where high penetration of renewables and decentralized generation require robust and scalable PF solutions. In experiments, all three proposed methods demonstrate a strong ability to provide an initial guess for Newton-Raphson method to converge with fewer steps. The findings provide a pathway for more efficient real-time grid operations, which, in turn, support the transition toward smarter and more resilient electricity networks.

Authors 11

1. Shengyuan Yan

   Eindhoven University of Technology

   Affiliation as printed

   Mathematics and Computer Science , Eindhoven University of Technology , P.O. Box 513 , 5600 MB , Eindhoven , The Netherlands

2. Farzad Vazinram

   University of Twente

   Affiliation as printed

   Computer Science , University of Twente , P.O. Box 217 , 7500 AE , En- schede , The Netherlands

3. Zeynab Kaseb

   Delft University of Technology

   Affiliation as printed

   Electrical Sustainable Energy , Delft University of Technology , P.O. Box 5031 , 2600 GA Delft , The Netherlands

4. Lindsay Spoor Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science , Leiden University , P.O. Box 9512 , 2300 RA Leiden , The Netherlands

5. Jochen Stiasny

   Delft University of Technology

   Affiliation as printed

   Electrical Sustainable Energy , Delft University of Technology , P.O. Box 5031 , 2600 GA Delft , The Netherlands

6. Betul Mamudi

   Delft University of Technology

   Affiliation as printed

   Electrical Sustainable Energy , Delft University of Technology , P.O. Box 5031 , 2600 GA Delft , The Netherlands

7. Amirhossein Heydarian Ardakani

   University of Twente

   Affiliation as printed

   Engineering Technology , University of Twente , P.O. Box 217 , 7500 AE , Enschede , The Netherlands

8. Ugochukwu Orji

   Tilburg University

   Affiliation as printed

   Jheronimus Academy of Data Science , Tilburg University , P.O. Box 90153 , 5037 AB , Tilburg , The Netherlands

9. Pedro P. Vergara

   Delft University of Technology

   Affiliation as printed

   Electrical Sustainable Energy , Delft University of Technology , P.O. Box 5031 , 2600 GA Delft , The Netherlands

10. Xiang Mei Yu

    Eindhoven University of Technology

    Affiliation as printed

    Alliander N.V. , P.O. Box 50 , 6920 AB , Arnhem , The Netherlands

    Electrical Engineering , Eindhoven University of Technology , P.O. Box 513 , 5600 MB , Eindhoven , The Netherlands

11. Jerry Jinfeng Guo
    Affiliation as printed

    Alliander N.V. , P.O. Box 50 , 6920 AB , Arnhem , The Netherlands