NDLI: Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production

Content Provider	IET Digital Library
Author	Jannat, Mohamed B. Savić, Aleksandar S.
Abstract	The paper proposes a new method for optimal allocation of shunt capacitors in distribution network with renewable distributed generation units such as wind turbines and/or solar power plants. The proposed method, based on application of the Monte Carlo simulation methods, respects the uncertainties associated with load demand and renewable sources power production. The optimisation problem is modelled by two criterion functions with the aim of improving voltage profile in a network by optimal selection of the locations and installed powers of the shunt capacitors. The suggested criterion functions have been optimised simultaneously by applying the non-dominated sorting genetic algorithm. On the basis of the measured data on wind speed and solar radiation, the method has been tested by the example of a real distribution network.
Starting Page	3060
Ending Page	3067
Page Count	8
ISSN	17518687
Volume Number	10
e-ISSN	17518695
Issue Number	Issue 12, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/10/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2016.0192
Journal	IET Generation, Transmission & Distribution
Publisher Date	2016-09-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Active Power Load Criterion Function Distributed Power Generation Distribution Network Genetic Algorithm Load Demand Monte Carlo Method Monte Carlo Simulation Method Nondominated Sorting Genetic Algorithm Optimal Capacitor Placement Optimisation Problem Optimisation Technique Renewable Distributed Generation Units Renewable Source Power Production Solar Power Plant Solar Radiation Voltage Profile Wind Speed Wind Turbine
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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