Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Christian A Hans, Pantelis Sopasakis, Jörg Raisch, Carsten Reincke-Collon, Panagiotis Patrinos

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2 Citations (Scopus)
87 Downloads (Pure)

Abstract

In this paper, we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimization problems, which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically constrained quadratic problem and its favorable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors.
Original languageEnglish
Number of pages16
JournalIEEE Transactions on Control Systems Technology
DOIs
Publication statusPublished - 08 Aug 2019

Keywords

  • Probability distribution
  • Stochastic processes
  • Load modeling
  • Microgrids
  • Computational modeling
  • Predictive models
  • Optimization
  • Average value-at-risk (AVaR)
  • energy management
  • islanded microgrids
  • model predictive control (MPC)
  • operation control
  • risk-averse control

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