A Novel Stochastic Predictive Stabilizer for DC Microgrids Feeding CPLs

Elham Kowsari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Tomislav Dragicevic, Mohammad Hassan Khooban

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In this work, a novel nonlinear approach is proposed for the stabilization of microgrids with constant power loads (CPLs). The proposed method is constructed based on the incorporation of a pseudo-extended Kalman filter into stochastic nonlinear model predictive control (MPC). In order to achieve high-performance and optimal control in DC microgrids, estimating the instantaneous power flow of the uncertain constant power loads and the available power units is essential. Thus, by utilizing the advantages of the stochastic nonlinear model predictive control and the pseudo-extended Kalman filter, an effective control solution for the stabilization of DC islanded microgrids with CPLs is established. This technique develops a constrained controller for practical application to handle the states and control input constraints explicitly; furthermore, as it estimates the current by using the pseudo-EKF, it is a current-senseless approach. As noisy measurements are taken into account for the state estimation, it leads to a less conservative control action rather than the classical robust MPC, whereas it guarantees the global asymptotic stability in the presence of noisy measurements and parameter uncertainty. To validate the performance of the proposed controller, the attained results are compared to state-of-the-art controllers. Furthermore, the implementability of the proposed method is validated using real-time simulations on dSPACE hardware.
Original languageEnglish
JournalIEEE Journal of Emerging and Selected Topics in Power Electronics
Number of pages10
Publication statusAccepted/In press - 2021


  • DC microgrid
  • Constant power load
  • Model-in-the-Loop
  • Nonlinear dynamic
  • Stochastic model predictive control
  • Extended Kalman filter


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