Optimal allocation and sizing of embedded generators

Rahman, Titik Khawa Abdul; Rahim, Siti Rafidah Abdul; Musirin, Ismail

doi:10.1109/pecon.2004.1461660

Cited by 50 publications

(21 citation statements)

References 11 publications

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“…If the method which was used in [41][42][43] is employed here for DGs optimal size recognition, different size of DGs should be assumed for size optimization. For instance in the specified example, possible conditions of installation are listed in Table 2, there are 4 DGs of same size.…”

Section: Exhaustive Searchmentioning

confidence: 99%

“…Optimal installation buses of DGs are recognized. A new method for size optimization is proposed by dividing DGs to small modules and reserving the opportunity of installing more than one module on each bus, unlike most other researches which have been done by checking the installation of dedicated different size of DGs [41][42][43]. Optimal sizes of DGs are hand out by summing up total power of all installed DGs on a single bus.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

Pesaran

Zin

Khairuddin

et al. 2015

Distributed Generation & Alternative Energy Journal

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Dispersed generation generally refers to power generation on the customer side of a power network. This article demonstrates the improvement in network parameters which could be achieved by using single and multiple Distributed Generation units in three selected standard networks. A reliable multi variable method is used for finding optimal installation point and size of distributed generation units. Their sites and sizes are recognized by implementation of a proposed method with exhaustive search. Optimization has been applied on network total active and reactive losses together with voltage variation. IEEE 6, 14 and 30 buses standard networks have been selected as study cases. The total active and reactive power losses are minimized while voltage profile is improved by installing Distributed Generation units on recognized optimal points with achieved optimal size.

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Section: Exhaustive Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

Pesaran

Zin

Khairuddin

et al. 2015

Distributed Generation & Alternative Energy Journal

View full text Add to dashboard Cite

show abstract

“…1 c and 2 c are cognitive parameter (local acceleration) and social parameter (Global acceleration), respectively. 1 r and 2 r are random numbers, uniformly distributed with in the interval [0,1].…”

Section: T T V T C T R P T X T C T R G T X Tmentioning

confidence: 99%

“…Limitation of space and the slow imp rovement of the networks also caused some areas with high load density which could result in declining of power quality and voltage collapse. At the same time, non-urban areas are witnessing poor performance of the networks like high voltage drops and high losses along the distribution lines [1]. In contrast, despite limitations in networks and availab le financial resources, utilities are also hardly trying to expand and boost networks.…”

Section: Introductionmentioning

confidence: 99%

Economic Designing of PV/FC/Wind Hybrid System Considering Components Availability

Arabi¹,

Hajibeigy²

2013

IJMECS

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Abstract-This paper presents an Optimizat ion Sizing of a stand-alone PV/FC/Wind Hybrid System (PFWHS) to optimize the sizes of co mponents of PFWHS. Based on PSO algorith m, one optimal sizing method was developed to determine the optimal configuration of system that can achieve the load required power supply probability (Reliab ility) with a minimu m overall cost of energy (OCE).PFW HS costs involve investments, operation and maintenance as well as loss of load costs. The applied wind and radiat ion datasets belong to northwest region (Jolfa, latitude: 38_56, longitude: 45_ 37, altitude: 710, m) of Iran. In this paper the impact of availability of PFWHS co mponents is investigated on optimal sizing. So, to investigating the impact of component availability rate on PFWHS costs and reliability due to failure and repair rate of PFW HS components and uncertainty in wind speed and solar irradiance, t wo scenarios are considered. In first scenario the availab ility of all co mponents is considered 1 and in second scenario isn't considered 1 because of failure and repair rate of co mponents and uncertainty in wind speed and solar irradiance. Obtained results prove that while the overall cost of energy is optimized, the reliability indices are within a satisfactory bound with regard to the reliab ility standards. Also the results indicate that considering co mplete availability of components causes more reliability rate, so to achieve the actual behavior of PFW HS, the rate of co mponents availability must be considered in suitable insight to designer for supply the load.

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“…The authors first found the weighting factor (WF) then DG location in the network system and then the size of DG in three separate steps. T. K. A. Rahman et al discussed the sizing and allocation of DG in two separate steps using the evolutionary programming (EP) techniques [2]. The authors first solved for the optimal location and then for DG size.…”

Section: Introductionmentioning

confidence: 99%

Parametric Performance Evaluation of Different Types of Particle Swarm Optimization Techniques Applied in Distributed Generation System

Kumar

Pal

et al. 2013

Advances in Intelligent Systems and Computing

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Abstract. This paper presents performance comparative study of various particle swarm optimization (PSO) techniques for the placement of generator units in the distributed generation (DG) system. For the installation of generator units in the distributed generation system, it is very important to know the generator sizing and its placement in the network system for reducing the line losses and hence the cost. Various PSO techniques such as Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO and Time varying inertia weight (TVIW) PSO have been used for comparative study. Here the main objective function (OF) is to minimize the system cost. These techniques have been tested on the standard IEEE-14 bus, IEEE-30 bus and IEEE-57 bus network system by the use of MATLAB software.

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Optimal allocation and sizing of embedded generators

Cited by 50 publications

References 11 publications

Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

Economic Designing of PV/FC/Wind Hybrid System Considering Components Availability

Parametric Performance Evaluation of Different Types of Particle Swarm Optimization Techniques Applied in Distributed Generation System

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