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Microgrid Optimal Scheduling With Multi-Period Islanding Constraints

Khodaei, Amin·2014·IEEE Transactions on Power Systems

Abstract

This paper presents a model for microgrid optimal scheduling considering multi-period islanding constraints. The objective of the problem is to minimize the microgrid total operation cost which comprises the generation cost of local resources and cost of energy purchase from the main grid. The microgrid optimal scheduling problem is decomposed into a grid-connected operation master problem and an islanded operation subproblem. The microgrid capability in operating in the islanded mode for multiple hours is scrutinized by a T-τ islanding criterion. The integer scheduling decisions determined in the master problem will be examined against the microgrid islanding feasibility in the subproblem. The scheduling decisions will be revised using proper islanding cuts if sufficient generation is not available to guarantee a feasible islanding. Islanding cuts will revise generating units, energy storage systems, and adjustable loads schedules. Any change in the schedule of adjustable loads outside the operating time interval specified by consumers is penalized by an inconvenience factor in the objective. Numerical simulations demonstrate the effectiveness of the proposed microgrid optimal scheduling model and explore its economic and reliability merits.

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Metadata

Authors
Khodaei, A.
Venue
IEEE Transactions on Power Systems
Published
2014-05
Volume
29
Issue
3
Pages
1383-1392
ISSN
  • 0885-8950 (ISSN-L)
  • 1558-0679
OpenAlex
W2046944130
Cited by
387
References
0
FWCI
42.80
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Microgrid Optimal Scheduling With Multi-Period Islanding Constraints. https://app.cochat.ai/papers/10.1109/tpwrs.2013.2290006
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Khodaei, A. (2014). Microgrid Optimal Scheduling With Multi-Period Islanding Constraints. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2013.2290006

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.