Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Abu-Mouti, F. S.; El-Hawary, M.E.

doi:10.1109/tpwrd.2011.2158246

Cited by 646 publications

(287 citation statements)

References 21 publications

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“…Several approaches have been developed to place and size DG units for loss reduction due to its impact on the utilities' revenue. Typical examples are analytical methods [19][20][21], numerical approaches [22][23][24] and a wide range of heuristic algorithms such as Genetic Algorithm (GA) [25], Particle Swarm Optimization (PSO) [26] and artificial bee colony algorithm [27]. Moreover, in recent years, due to shar-ply increased loads and the demand for higher system security, DG allocation for voltage stability at the distribution system level has attracted the interest of some recent research efforts.…”

Section: Introductionmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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highlights DG allocation for minimizing energy loss and enhancing voltage stability. Expressions to find the optimal power factor of DG with commercial standard size. A methodology for DG planning to recover investment for DG owners.Impact of technical and environmental benefits on DG investment decisions. Benefit-cost analysis to specify the optimal location, size and number of DG units. Keywords:Distributed generation; Emission reduction; Loss reduction; Network upgrade deferral; Optimal power factor; Voltage stability abstract This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit-cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery.

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Section: Introductionmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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“…The optimal penetration level is directly identified from this convergence characteristic curve. The results obtained in this proposed algorithm are compared with the results reported in existing literature [8] and [22]. The comparison of results in number of DG units, penetration level and real power losses is also tabulated in Tab.…”

Section: Case II -Ieee 57 Bus Systemmentioning

confidence: 94%

“…Due to the requirement of more data that algorithm is difficult to implement [8]. Planning for DG is discussed, which includes capital cost, operating cost, planning of emergency limits and normal limits [9].…”

Section: Introductionmentioning

confidence: 99%

PSO based optimal distributed generation placement and capacity by considering harmonic limits

Sasiraja

Muthulakshmi²,

Kumar³

et al. 2017

Teh. vjesn.

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Original scientific paper Distributed Generation (DG) units are also called Dispersed Generation, Decentralized Generation and Embedded Generation. They are normally small generating plants, connected directly to either distribution side or customer side. The installation of inverter-based distributed generation (DG) has increased rapidly in recent years. This higher penetration level may result in the increased level of harmonics, which could exceed the permissible harmonic distortion level. The penetration level of DG is restricted by harmonic distortion, because of the nonlinear current injected by inverter based DG units. In this work, the maximum DG penetration level is determined, by considering the harmonic limits. The harmonics are determined by using the Decoupled Harmonic Power Flow (DHPF) approach. The constraints of this proposed problem include power balance equations, bus voltage limits, total and individual harmonic distortion limits specified by IEEE-519 standard. The problem is solved by using Particle Swarm Optimization (PSO) algorithm based optimization technique. Simulation results are obtained by MATPOWER/MATLAB in IEEE 30 and IEEE 57 bus test systems and the results prove the effectiveness of this proposed approach. Keywords: Decoupled Harmonic Power Flow (DHPF); Distributed Generation (DG); harmonic distortion; Particle Swarm Optimization (PSO)Na optimizaciji roja čestica utemeljeno optimalno razmještanje i kapacitet decentralizirane proizvodnje uzimajući u obzir harmonijska ograničenjaIzvorni znanstveni članak Jedinice decentralizirane proizvodnje (DG units) također se nazivaju raspršenom proizvodnjom, decentraliziranom proizvodnjom i ugrađenom proizvodnjom. Uobičajeno su to male elektrane, direktno povezane bilo s distribucijom ili potrošačem. Uspostava decentralizirane proizvodnje na temelju invertera rapidno se povećala zadnjih godina. To može biti rezultat povećane razine sekundarne frekvencije koja može prekoračiti dopušenu razinu harmonijskog izobličenja. Razina prodiranja DG ograničena je harmonijskim izobličenjem zbog nelinearne struje iz DG jedinica na bazi invertera. U ovom je radu određena maksimalna razina penetracije DG, uzimajući u obzir harmonijska ograničenja. Harmonici su određeni pristupom odvojenog harmonijskog protoka snage (DHPF). Ograničenja ovog predloženog problema uključuju jednadžbe balansiranja snage, ograničenja napona sabirnice, ukupne i pojedinačne granice harmonijskog izobličenja specificiranog normom IEEE-519. Problem je riješen primjenom tehnike optimizacije pomoću algoritma optimizacije roja čestica. Rezultati simulacije dobiveni su primjenom MATPOWER/MATLAB u sustavima ispitivanja sabirnice IEEE 30 i IEEE 57, a rezultati dokazuju učinkovitost predloženog pristupa.Ključne riječi: decentralizirana proizvodnja; harmonijsko izobličenje; odvojeni harmonijski protok snage; optimizacija roja čestica

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“…The unbalanced three phase distribution load flow has been performed using OpenDSS tool and IEEE 123 node test feeder is used as test bed. In [48], an optimization approach based on artificial bee colony (ABC) algorithm is used to determine the optimal size, power factor and location of DG unit to minimize the total system power loss. These techniques give a single or multi (2)(3)(4) large DG units in the distribution network.…”

Section: Network Dg Capacity Assessmentmentioning

confidence: 99%

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Kamdar

Karady

2014

2014 Clemson University Power Systems Conference

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With ever increasing use of natural gas to generate electricity, installed natural gas fired microturbines are found in residential areas to generate electricity locally. This research work discusses a generalized methodology for assessing optimal capacity and locations for installing natural gas fired microturbines in distribution residential network.The overall objective is to place microturbines to minimize the system power loss occurring in the electrical distribution network; in such a way that the electric feeder does not need any up-gradation. The IEEE 123 Node Test Feeder is selected as the test bed for validating the developed methodology. Three-phase unbalanced electric power flow is run in OpenDSS through COM server, and the gas distribution network is analyzed using GASWorkS. The continual sensitivity analysis methodology is developed to select multiple DG locations and annual simulation is run to minimize annual average losses.The proposed placement of microturbines must be feasible in the gas distribution network and should not result into gas pipeline reinforcement. The corresponding gas distribution network is developed in GASWorkS software, and nodal pressures of the gas system are checked for various cases to investigate if the existing gas distribution network can accommodate the penetration of selected microturbines. The results indicate the optimal locations suitable to place microturbines and capacity that can be accommodated by the system, based on the consideration of overall minimum annual average losses as well as the guarantee of nodal pressure provided by the gas distribution network. The proposed method is generalized and can be used for any IEEE test feeder or an actual residential distribution network.ii ACKNOWLEDGMENTS

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Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Cited by 646 publications

References 21 publications

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

PSO based optimal distributed generation placement and capacity by considering harmonic limits

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

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