A Linear Adaptive Robust Optimization Model for Day-Ahead Scheduling of Microgrid

Zadeh, Mahmoud Mollayousefi; Afshar, Zakaria; Heydari, Rasool; Bathaee, S.M.T.; Savaghebi, Mehdi

doi:10.1109/iecon43393.2020.9254928

Cited by 1 publication

(6 citation statements)

References 15 publications

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“…Equation ( 2) shows the OF of the frst stage, which minimizes the start-up and constant costs of the DGs. Te constraints defned by Ω 1 (X) are related to the start-up and shutdown constraints of the DG units [27].…”

Section: Modelling and Formulationmentioning

confidence: 99%

“…Te ARO approach considers diferent ULs to adjust the robustness of the solution. To apply ARO, the uncertainties of PV and WT powers, electricity demand, and price are considered as polyhedral uncertainty sets for each uncertain parameter, i.e., Θ ∈ Ω 2 set presented in [27].…”

Section: Modelling and Formulationmentioning

confidence: 99%

“…In order to formulate the ARO subproblem in a linear form, an approximation of the AC power fow equations is required. As such, ( 20) and ( 21) are substituted with a linear approximation of the power fow equations in the second stage, as described by the following equation [27]:…”

Section: Linearized and Convexifed Ac Power Flow Equationsmentioning

confidence: 99%

“…Te unit commitment status is determined after solving the frst-stage problem. Te second stage is the ARO subproblem, a maximization problem formulated as [27]. Te iteration between the frst-stage problem and the ARO subproblem will be in process until the satisfaction of the convergence criterion.…”

Section: Solution Algorithmmentioning

confidence: 99%

“…With the objective of exhibiting the efcacy of the proposed model and solution algorithm, a 33bus test system is evaluated. Tis benchmark system is identical to the ref [27] which includes 4 dispatchable units, 3 PV stations, 2 WTs, and 1 battery energy storage system. Tis system was chosen in order to compare the results with the reference paper.…”

Section: Case Study Systemmentioning

confidence: 99%

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An Adaptive Robust Optimization Model for Microgrids Operation Using Convexified AC Power Flow Equations

Mollayousefi Zadeh,

Afshar,

Farahani

et al. 2023

International Transactions on Electrical Energy Systems

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Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods.

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Section: Modelling and Formulationmentioning

confidence: 99%

Section: Modelling and Formulationmentioning

confidence: 99%

Section: Linearized and Convexifed Ac Power Flow Equationsmentioning

confidence: 99%

Section: Solution Algorithmmentioning

confidence: 99%

Section: Case Study Systemmentioning

confidence: 99%

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