A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner's and Distribution Company's Viewpoints

Ameli, Amir; Bahrami, Shahab; Khazaeli, Farid; Haghifam, Mahmoud Reza

doi:10.1109/tpwrd.2014.2300845

Cited by 249 publications

(142 citation statements)

References 25 publications

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“…The proper size of DG gives the positive benefits in the distribution systems. Voltage profile improvement, loss reduction, distribution capacity increase and reliability improvements are some of the benefits of system with DG placement (Rahim et al 2013;Ameli et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Suresh

Edward

2018

Renewables

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Distributed generation (DG) is small generating plants which are connected to consumers in distribution systems to improve the voltage profile, voltage regulation, stability, reduction in power losses and economic benefits. The above benefits can be achieved by optimal placement of DGs. A novel nature-inspired algorithm called Dragonfly algorithm is used to determine the optimal DG units size in this paper. It has been developed based on the peculiar behavior of dragonflies in nature. This algorithm mainly focused on the dragonflies how they look for food or away from enemies. The proposed algorithm is tested on IEEE 15, 33 and 69 test systems. The results obtained by the proposed algorithm are compared with other evolutionary algorithms. When compared with other algorithms the Dragonfly algorithm gives best results. Best results are obtained from type III DG unit operating at 0.9 pf.

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Section: Introductionmentioning

confidence: 99%

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Suresh

Edward

2018

Renewables

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“…(1) Traditional LF solvers: The traditional LF solution methods, aiming at solving equality and inequality constraints, predominately include: • Other LF methods and frameworks as in [33,[42][43][44]51,53,66,80,90,92]; in addition to LF frameworks as multi-criteria stochastic planning model (MCSPM) with central limit theorem (CLT) [34] and IBVT in [56].…”

Section: Load Flow Solution Methods (S)mentioning

confidence: 99%

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper.Keywords: active distribution network (ADN); distributed generation (DG); distributed energy resources (DERs); distribution network planning (DP); multi-objective optimization (MOO); multi-criteria decision analysis (MCDA); distributed generation placement (DGP); volt-ampere reactive power (VAR) compensation and power quality (VPQ); component reinforcement and up gradation (CRU); network (distribution) topology change and reconfiguration (NTR); planning techniques (PT); multiple objective planning (MOP); multi-objective planning techniques (MOPTs); future distribution networks (FDNs)

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“…If f(x) consists of an objective functions, then the multiobjective problem can be defined as finding the vector * = 1 * , 2 * … * in order to minimize f(x) = 1 2 … ( ) subject to * ∈  [12,14]. In the minimization problem, the solution x1 dominates x2 if 1) ∀ ∈ 1,2, … : ( 1 ) ≤ ( 2 ) (7) 2) ∃ ∈ 1,2, … : ( 1 ) < ( 2 ) (8) Like PSO, in the MOPSO algorithm, each particle at the time t is introduced by two borders, its velocity ( ) and its position ( ).…”

Section: Fig 1 Flowchart Of the Multi-objective Pso Methodsmentioning

confidence: 99%

Placement and Sizing of Distributed Generation on Distribution Systems with a Multi-Objective Particle Swarm Optimization and Analytical Approach: A Review

Shivwanshi¹,

Elias²

2017

IJCA

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The Distributed generations (DGs) have number of benefits in the electric power industry, such as improvement of voltage stability, enhancement of reliability and power quality. This paper compares the DG placement result of analytical approach with the Multi-Objective Particle Swarm Optimization (MOPSO). The analytical method is based on a formulation for the power flow problem. A priority is loss sensitivity to determine the best locations of applicant distributed generation units. The multi-objective particle swarm optimization determines the optimal DGs places and sizes. The MOPSO improves voltage profile and stability, power-loss reduction, and reliability enhancement. The results show that the analytical method could lead to optimal or nearoptimal result, while requiring lower computational effort.

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A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner's and Distribution Company's Viewpoints

Cited by 249 publications

References 25 publications

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Placement and Sizing of Distributed Generation on Distribution Systems with a Multi-Objective Particle Swarm Optimization and Analytical Approach: A Review

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