• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Model predictive control for reactive power management in transmission connected distribution grids
 
  • Details
  • Full
Options
2016
Conference Paper
Title

Model predictive control for reactive power management in transmission connected distribution grids

Abstract
In this work a multi-objective model predictive control for reactive power management in transmission connected distribution grids with high share of wind power is presented. The proposed control utilizes reactive power capabilities of wind farms and tap-changer positions in order to improve distribution grid operation. Control signals namely tap-changer positions and reactive power set-points are smoothed over the forecast horizon. Further possible optimization objectives are power loss reduction, voltage profile smoothing and complying with reactive power exchange limits with the transmission grid. A mixed-integer non-linear optimal power flow problem (MINLP-OPF) is formulated incorporating grid operation limits. The performance is evaluated on a real German 110-kV distribution grid with 1.6 GW wind power for one year. With the proposed control, reactive power exchange within allowable limits is increased from 58.3% to 94.5%, compared to a reference operation where on ly tap-changer positions are utilized for loss reduction with a single time-step optimization.
Author(s)
Stock, D.S.
Venzke, A.
Hennig, T.
Hofmann, L.
Mainwork
IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2016  
Conference
Asia-Pacific Power and Energy Engineering Conference (APPEEC) 2016  
DOI
10.1109/APPEEC.2016.7779538
Language
English
Fraunhofer-Institut für Windenergiesysteme IWES  
Keyword(s)
  • reactive power

  • optimization

  • Load Flow

  • Software

  • Model Predictive Control (MPC)

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024