Stochastic Predictive Energy Management of Multi-Microgrid Systems

Najmeh Bazmohammadi, Amjad Anvari-Moghaddam, Ahmadreza Tahsiri, Ahmad Madary , Juan C. Vasquez, Josep M. Guerrero*

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

10 Citations (Scopus)
14 Downloads (Pure)


Next-generation power systems will require innovative control strategies to exploit existing and potential capabilities of developing renewable-based microgrids. Cooperation of interconnected microgrids has been introduced recently as a promising solution to improve the operational and economic performance of distribution networks. In this paper, a hierarchical control structure is proposed for the integrated operation management of a multi-microgrid system. A central energy management entity at the highest control level is responsible for designing a reference trajectory for exchanging power between the multi-microgrid system and the main grid. At the second level, the local energy management system of individual microgrids adopts a two-stage stochastic model predictive control strategy to manage the local operation by following the scheduled power trajectories. An optimal solution strategy is then applied to the local controllers as operating set-points to be implemented in the system. To distribute the penalty costs resulted from any real-time power deviation systematically and fairly, a novel methodology based on the line flow sensitivity factors is proposed. Simulation and experimental analyses are carried out to evaluate the effectiveness of the proposed approach. According to the simulation results, by adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the multi-microgrid system with the main grid can be achieved.
Original languageEnglish
Article number4833
JournalApplied Sciences
Issue number14
Pages (from-to)1-16
Number of pages16
Publication statusPublished - Jul 2020


  • interconnected microgrids
  • Energy Management System
  • stochastic optimization
  • model predictive control
  • line sensitivity factors


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