Probabilistic Multi-Objective Optimal Power Flow in an AC/DC Hybrid ‎Microgrid Considering Emission Cost

Document Type : Research paper

Authors

Department of Electrical Engineering, Razi University, Kermanshah, Iran.‎

Abstract

As a basic tool in power system control and operation, the optimal power flow (OPF) problem searches the optimal operation point via minimizing different objectives and maintaining the control variables within their applicable regions. In recent years, this problem has encountered many challenges due to the presence of renewable energy sources, which has led introducing of a combinatorial type of power networks known as AC/DC hybrid power systems. In this paper, the OPF problem is proposed in an AC/DC hybrid microgrid, including wind power plants. For the first time, the mentioned problem is considered as a multi-objective optimization problem via minimizing fuel cost and emission. The problem is modeled while considering the power flow equations, the voltage limits in AC and DC buses, the AC voltage angle limits, and the firing angle of the converters. Also, due to the uncertain power generated by wind power plants, the probabilistic OPF problem is modeled by the five-point estimation method.  To solve the problem, the "fmincon" function in MATLAB software is used by applying the IP algorithm. The simulation case study on a 13-bus sample MG verifies the effectiveness of the proposed method. The numerical results confirm that increasing the wind farm capacity from 14.54 MW to 113 MW, will be led to increasing the fuel cost from 10% to 61%, in case of including the power losses compared to the condition in which they are neglected. It is also observed that in terms of different weights, the total air pollution including the power losses is 2.30 to 2.40 times higher than the total pollution without electrical losses

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 10, Issue 1
April 2022
Pages 13-27

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History

  • Receive Date: 15 December 2020
  • Revise Date: 21 February 2021
  • Accept Date: 27 March 2021

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