Najafi, A., Aboli, R., Falaghi, H., Ramezani, M. (2016). Capacitor Placement in Distorted Distribution Network Subject to Wind and Load Uncertainty. Journal of Operation and Automation in Power Engineering, 4(2), 153-164.

A. Najafi; R. Aboli; H. Falaghi; M. Ramezani. "Capacitor Placement in Distorted Distribution Network Subject to Wind and Load Uncertainty". Journal of Operation and Automation in Power Engineering, 4, 2, 2016, 153-164.

Najafi, A., Aboli, R., Falaghi, H., Ramezani, M. (2016). 'Capacitor Placement in Distorted Distribution Network Subject to Wind and Load Uncertainty', Journal of Operation and Automation in Power Engineering, 4(2), pp. 153-164.

Najafi, A., Aboli, R., Falaghi, H., Ramezani, M. Capacitor Placement in Distorted Distribution Network Subject to Wind and Load Uncertainty. Journal of Operation and Automation in Power Engineering, 2016; 4(2): 153-164.

Capacitor Placement in Distorted Distribution Network Subject to Wind and Load Uncertainty

^{}Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

Receive Date: 13 July 2015,
Revise Date: 08 April 2016,
Accept Date: 25 December 2016

Abstract

Utilizing capacitor banks is very conventional in distribution network in order for local compensation of reactive power. This will be more important considering uncertainties including wind generation and loads uncertainty. Harmonics and non-linear loads are other challenges in power system which complicates the capacitor placement problem. Thus, uncertainty and network harmonics have been considered in this paper, simultaneously. Capacitor placement has been proposed as a probabilistic harmonic problem with different objectives and technical constraints in the capacitor placement problem. Minimizing power and energy loss and capacitor prices are considered as objectives. Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms have been used to solve the optimization problem. Loads are subjected to uncertainty with normal probabilistic distribution function (PDF). Auto Regressive and Moving Average (ARMA) time series and two point estimate method have also been utilized to simulate the wind speed and to perform the probabilistic load flow, respectively. Finally, the proposed method has been implemented on standard distorted test cases in different scenarios. Monte Carlo Simulation (MCS) has also been used to verify the probabilistic harmonic power flow. Simulation results demonstrate the efficiency of the proposed method.

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