Optimal Distributed Generator Sizing and Placement by Analytical Method and Fuzzy Expert System: a Case Study in Tezpur University, India

Manas, Munish; Saikia, Biplob Jyoti; Baruah, D.C.

doi:10.1007/s40866-018-0038-9

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…However, the results of power losses minimization for the 0 -3 MW limits have are obtained to be 68.56%, 91.57% and 71.15% that corresponds to one, two and three DGs at optimal locations and sizing. The percentage of power losses reduction can be calculated using equation (10) where and indicates the total power loss without and with DG, respectively.…”

Section: Optimal Allocation Of the Dg And Total Power Loss Minimizatimentioning

confidence: 99%

“…Other knowledge contributions include a combination of analytical and computing technique with the aim of achieving comparison in power loss minimization and relative efficiency on the model. The comparison between the analytical and fuzzy method in [10] have proposed a methodology that comprises of two-stage approach to achieve optimal sizing as well as placement of DGs. For finding optimal sizing and location of DG, there are quite a number of algorithms which have been employed.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Musa

Hashim

2019

IJEECS

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This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

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Section: Optimal Allocation Of the Dg And Total Power Loss Minimizatimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Musa

Hashim

2019

IJEECS

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“…Authors have considered minimization of active power losses as an objective and tested on IEEE 33 and PG& E 69 bus distribution systems. Fuzzy expert system based approach was developed in [12] for optimal placement of DG units. Optimal placement was identified based on Distribution Loss Reduction Index (DLRI) and Voltage Deviation Reduction Index (VDRI).…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Lakshmi

Jayalaxmi

Veeramsetty

2021

Technol Econ Smart Grids Sustain Energy

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Optimal placement of distributed generation (DG) is an essential task for distribution companies in order to operate the distribution network in good operating conditions. Optimal placement of DG units is an optimization problem where minimization of active power losses in the network is considered as an objective function. In this paper, hybrid genetic dragonfly algorithm is used as an optimization technique to find the optimal location and size of distributed generation units. The proposed algorithm is implemented on IEEE 15 and PG & E 69 bus distribution systems in MATLAB environment. Based on the simulation results it has been observed that with proper placement and size of DG units, distribution network can be operated with less active power losses.Keywords Distributed generation • Optimal placement • Hybrid genetic dragonfly algorithm • Active power loss Nomenclature ΔY t+1 Step vector for dragonflies A iAlignment value of dragonfly i C i Cohesion value of dragonfly i E iEnemy source of dragonfly i F iFood source of dragonfly i I k i Current at bus 'i' and iteration 'k' I k LR Current injecting at receiving node of line 'L' at iteration 'k' I l Current flowing through line l I k L Current through line 'L' at iteration 'k' I max l

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“…ALO has been also presented to find the optimal size of DGs while index vector method is utilized to find the optimal locations [ [29]). In [30], the sizing of DGs is done by analytical approach based on exact loss formula for minimum loss at the allied buses. After that Fuzzy expert system is used for optimal placement using distribution loss reduction index and voltage deviation reduction index as its input parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Energy Resources Allocation for Enriching Reliability and Economic Benefits Using Sine-Cosine Algorithm

Abdelsalam

2020

Technol Econ Smart Grids Sustain Energy

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This paper presents an optimization algorithm called sine-cosine algorithm (SCA) for optimal distributed energy resources (DERs) allocation in various configurations of radial distribution networks. This study is demonstrated in two statuses to find the optimal locations and sizes of DERs to be installed on the distribution system. First status; the most candidate locations for connecting DERs are suggested by using the loss sensitivity factor theory and the proposed SCA is applied to select the optimal capacities of DERs. Second status; the SCA is used to determine both the optimal locations and sizes of DERs. The positive impact of DERs on distribution systems reliability has been investigated in addition to the system power losses and bus voltages. The fitness function is to maximize the savings produced by not only power losses reduction but also reliability enhancement. The proposed algorithm is applied to IEEE 33 and 69-bus radial distribution networks with installing different number of DERs. The simulation results using Matlab programming environment show that the proposed methodology is viable, supporting reliability as well as fulfilling the conventional objectives such as cost minimization, voltage profile improvement, system losses reduction. A comparison between SCA and other methods is introduced to verify the superiority of SCA where SCA fulfils the maximum saving and maximum reduction of power losses equals to 61.3% and 69.2% for IEEE 33-bus and 69-bus networks, respectively.

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Optimal Distributed Generator Sizing and Placement by Analytical Method and Fuzzy Expert System: a Case Study in Tezpur University, India

Cited by 16 publications

References 24 publications

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Optimal Distributed Energy Resources Allocation for Enriching Reliability and Economic Benefits Using Sine-Cosine Algorithm

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