Optimal placement of different type of DG sources in distribution networks
01 Dec 2013-International Journal of Electrical Power & Energy Systems (Elsevier)-Vol. 53, pp 752-760
TL;DR: The particle swarm optimization (PSO) technique has been used to solve the optimal placement of DGs and the optimal power factor for DG supplying, both real and reactive power, has been obtained.
About: This article is published in International Journal of Electrical Power & Energy Systems.The article was published on 2013-12-01. It has received 322 citations till now. The article focuses on the topics: AC power & Power factor.
Citations
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TL;DR: In this article, the authors present a review of recent optimization methods applied to solve the problem of placement and sizing of distributed generation units from renewable energy sources based on a classification of the most recent and highly cited papers.
345 citations
TL;DR: Fireworks Algorithm is used to simultaneously reconfigure and allocate optimal DG units in a distribution network using a new swarm intelligence based optimization algorithm conceptualized using the fireworks explosion process of searching for a best location of sparks.
281 citations
TL;DR: In this paper, a review of the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers is presented, and an attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal size and siting of DG in distribution network.
Abstract: To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.
266 citations
Cites background or methods from "Optimal placement of different type..."
...Location of wind operated DG by least lost method and optimization by particle swarm optimization (PSO) Minimize Real power loss Voltage profile [10,74]...
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...In PSO a set of arbitrarily provoked solutions moves in the design arena favoring the best solution over number of repetitions highlighted by [25,29,32,33,39,70,74]....
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...Particle Swarm Optimization algorithm (PSO) based technique High degree of accuracy and less converging time Suffers from the partial optimism, due to that its velocity and direction not maintained and inefficient for large and complex systems [51,70,74]...
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TL;DR: In this paper, a hybrid approach has been proposed for optimal placement of multiple DGs of multiple types in power distribution network for reduction of power loss, where the sizes of DGs are evaluated at each bus by analytical method while the locations are determined by PSO based technique.
214 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
TL;DR: In this article, the problem of capacitors placement on a radial distribution system is formulated and a solution algorithm is proposed, where the location, type, and size of the capacitors, voltage constraints, and load variations are considered.
Abstract: The problem of capacitor placement on a radial distribution system is formulated and a solution algorithm is proposed. The location, type, and size of capacitors, voltage constraints, and load variations are considered. The objective of capacitor placement is peak power and energy loss reduction, taking into account the cost of the capacitors. The problem is formulated as a mixed integer programming problem. The power flows in the system are explicitly represented, and the voltage constraints are incorporated. A solution method has been implemented that decomposes the problem into a master problem and a slave problem. The master problem is used to determine the location of the capacitors. The slave problem is used by the master problem to determine the type and size of the capacitors placed on the system. In solving the slave problem, and efficient phase I-phase II algorithm is used. >
1,832 citations
TL;DR: In this article, an analytical expression to calculate the optimal size and an effective methodology to identify the corresponding optimum location for DG placement for minimizing the total power losses in primary distribution systems is proposed.
1,060 citations
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.
Abstract: Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation (DG) sources. Proper location of DGs in power systems is important for obtaining their maximum potential benefits. This paper presents analytical methods to determine the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system. Simulation results are given to verify the proposed analytical approaches.
1,042 citations
TL;DR: A new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss.
Abstract: Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.
652 citations