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Journal ArticleDOI

An integrated approach for distributed resource allocation and network reconfiguration considering load diversity among customers

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TLDR
In the proposed dedicated search teaching learning based optimization (DSTLBO), new learning approaches are suggested to enhance convergence, accuracy and efficiency of the standard TLBO.
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This article is published in Sustainable Energy, Grids and Networks.The article was published on 2016-09-01. It has received 20 citations till now. The article focuses on the topics: Load profile & Resource allocation.

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Citations
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Journal ArticleDOI

An improved Elitist–Jaya algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems

TL;DR: The proposed approach uses a new forward-backward sweep based load flow solution to evaluate the objective function during different reconfiguration phases and considers both active power loss reduction and loadability enhancement without violating the system constraints.
Journal ArticleDOI

Day-ahead energy management and feeder reconfiguration for microgrids with CCHP and energy storage systems

TL;DR: This work proposes a framework that leverages scenarios to jointly manage the real and reactive power dispatches of the controllable generation resources as well as the topology of the distribution feeder to improve the technical and economic efficiency of microgrids.
Journal ArticleDOI

Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow

TL;DR: In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs.
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Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models

TL;DR: A new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads and is suitable for implementing in power system planning is presented.
Journal ArticleDOI

IEC 61850-based adaptive protection system for the MV distribution smart grid

TL;DR: The present work describes an innovative approach to the dynamic reconfiguration of protection devices used to implement advanced fault location, isolation and service restoration solutions based on the IEC 61850 standard adoption.
References
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Journal ArticleDOI

Network reconfiguration in distribution systems for loss reduction and load balancing

TL;DR: Accuracy analysis and the test results show that estimation methods can be used in searches to reconfigure a given system even if the system is not well compensated and reconfiguring involves load transfer between different substations.
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Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
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Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization

TL;DR: In this article, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss.
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Analytical approaches for optimal placement of distributed generation sources in power systems

TL;DR: In this article, the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system has been investigated to obtain the maximum potential benefits.
Journal ArticleDOI

Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation

TL;DR: In this paper, a meta heuristic Harmony Search Algorithm (HSA) is used to simultaneously reconfigure and identify the optimal locations for installation of DG units in a distribution network.
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