Stochastic-Predictive Energy Management System for Isolated Microgrids

Daniel E. Olivares, Jose D. Lara, Claudio A. Canizares, Mehrdad Kazerani

Research output: Contribution to journalArticlepeer-review

109 Scopus citations

Abstract

This paper presents the mathematical formulation and control architecture of a stochastic-predictive energy management system for isolated microgrids. The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach. The first stage decision variables (unit commitment) are determined using a stochastic mixed-integer linear programming formulation, whereas the second stage variables (optimal power flow) are refined using a nonlinear programming formulation. This novel approach was tested on a modified CIGRE test system under different configurations comparing the results with respect to a deterministic approach. The results show the appropriateness of the method to account for uncertainty in the power forecast.

Original languageEnglish
Article number7265071
Pages (from-to)2681-2693
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume6
Issue number6
DOIs
StatePublished - Nov 2015
Externally publishedYes

Keywords

  • Energy management system (EMS)
  • microgrid
  • model predictive control (MPC)
  • optimal dispatch
  • optimal power flow (OPF)
  • stochastic programming (SP)

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