A hierarchical energy management system for islanded multi-microgrid clusters considering frequency security constraints

Mohammadreza Mazidi*, Navid Rezaei, Fatemeh Jahanbani Ardakani, Maryam Mohiti, Josep M. Guerrero

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

With the widespread development of microgrids (MGs) in future smart distribution networks, a number of neighboring MGs can be connected and form a multi-microgrid (MMG) cluster. In this regard, the energy management of a MMG is challenging due to more complex components and higher degrees of uncertainty in a small region of power system. Likewise, in the islanded MMG (IMMG) clusters, due to the low-inertia and high intermittent energy delivery of renewable resources, the frequency security should be considered in the energy management. To address this issue, this paper proposes an energy management system (EMS) in which hierarchical control structure of IMMG clusters is precisely modeled. The proposed EMS aims to minimize total operation cost of IMMG cluster while sufficient primary and secondary reserves are scheduled to preserve frequency security in a predefined range. Besides, the proposed EMS provides optimal strategies for MGs to exchange energy and reserves during scheduling horizon. To consider operational uncertainties, the proposed EMS is formulated as a two-stage stochastic mixed-integer linear programming problem that guaranties the global optimal solution. The obtained results verify that through the proposed EMS, total operation cost of the IMMG cluster is minimized while the frequency can be cost-effectively preserved within a pre-defined secure range.
Original languageEnglish
Article number106134
JournalInternational Journal of Electrical Power and Energy Systems
Volume121
ISSN0142-0615
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Energy and reserve scheduling
  • Energy management system
  • Hierarchical control
  • Islanded multi-microgrid clusters
  • Two-stage stochastic optimization

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