Analyzing the Load Modelling Impacts on Uncertain Optimal Reactive Power Dispatch Problem by Using Grey Wolf Optimization

Document Type : Research Article

Authors

1 Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran

2 Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch

3 Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah,Iran

Abstract

Optimal Reactive Power Dispatch (ORPD) is an essential subject in the economic operation of power systems. This issue is generally an optimization constrained problem satisfying the dominant control parameters. Due to the non-linear nature of the ORPD problem, solutions include several optima, and deterministic methods may lead to poor performance. On the other hand, the diversity and stochastic nature of electrical loads, arising from renewable energy penetration in the power system create significant challenges in solving this problem. Therefore, stochastic methods are required to find the appropriate solutions. In this paper, the Monte Carlo Simulation (MCS) is used to model the uncertainty of loads. Static modeling methods implement the type of load modeling. The polynomial ZIP method is applied to solve the ORPD problem for the first time. Optimizing the control parameters by applying the Grey Wolf Optimization (GWO) and based on the IEEE 30-bus standard as a general model is performed. Due to this, in the proposed method, the minimum voltage level will be 0.4 per unit less than the other methods. Also, the rate of system losses is improved by 7.61% compared to the base-case network, but compared to the other methods, regardless of the load model, it has a 10.76% higher loss rate. The simulation results show that the load models have a significant effect on the ORPD problem, and this concept is completely and directly transferred to the operation of the power system, and power system stability, accordingly.

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Main Subjects

References

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Cham: Springer International Publishing, 2017, pp. 475-513.
Journal of Modeling and Simulation in Electrical and Electronics Engineering
Volume 1, Issue 3 - Serial Number 5
November 2021
Pages 27-34

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History

  • Receive Date: 19 September 2021
  • Revise Date: 17 November 2021
  • Accept Date: 12 January 2022

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