Determining the optimal location and sizing of distributed generation Unit using Particle Swarm Optimization algorithm

Prabha, D. Rama; Mageshvaran, R.; Raghunath, Endla; Raghuram, Guda

doi:10.1109/iccci.2012.6158888

Cited by 17 publications

(15 citation statements)

References 7 publications

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“…One of the most common objectives found in the literature is the minimization of the total power losses of the system. Different algorithms were applied to minimize total power losses, such as particle swarm optimization [5], genetic algorithm [6], and artificial bee colony [7,8]. Other single-objective DG optimization approaches focus on the minimization of total cost.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Darfoun¹,

El‐Hawary

2015

Electric Power Components and Systems

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Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Darfoun¹,

El‐Hawary

2015

Electric Power Components and Systems

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“…Therefore, it is important to consider an automated control methodology for intelligent economical operation of deregulated power system compromising DG units' output power availability and load fluctuations [7]- [8]. Different artificial intelligent techniques such as genetic algorisms, particle swam, hybrid bacterial foraging, and firefly optimization techniques can be used to handle the search for optimal solution for economic operation [9]- [10]. The solution should balance the power demand and the available power generations from DGs and network considering all operating constraints of all variables in the power system [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal utilization of automated distributed generation in smart grid using genetic algorithm

Hoballah

Ahmad

Shoush³

2019

IJEECS

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<span>Distributed generation (DG) is an essential attributor in smart grid to fulfill the uncontrollable increase in the demand for energy. Artificial intelligent optimization techniques are widely used within automation systems for guarantee the optimal operation and utilization of DG allocation on the day-ahead power scheduling. In this paper, the genetic algorithm technique used for obtaining the optimal utilization of the automated operation of distributed generation for power losses and total cost minimization as well as user comfort maximization considering all operating constraints technique. Distributed generation represented by fuel cells to supply part of the daily demand in the power system. The target is to apply decision-making strategy of smart operation for economical and reliable operation of power system. Concentrated fuel cell units considered representing the available DG at the load centers. The methodology applied to the 11-bus test system. The simulation results have demonstrated that the GA capability for full automation of DGs in a smart manner within the power system for economic and safe operation</span>

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“…It is because of the quality of solution both are in optimal site with corresponding size and time to convergence (Jamian, Aman, et al, 2012) . PSO and GA are the heuristic method often used to optimize size and site of DGs (Gomez et al, 2010) (Sookananta et al, 2010) (Prabha et al, 2012) (Sedighi et al, 2010) (Ismail et al, 2011) (Nabavi et al, 2011) (Gonzalez et al, 2012) (Haghdar and Shayanfar, 2010).…”

Section: Size and Site Of Dgs As A Penetration Of Rer Into Distributimentioning

confidence: 99%

“…DG is a technology giving opportunity to integrating RER into power system network (Gozel and Hocaoglu, 2009). From many researches, penetration of RER in the electrical power system, especially in the level 53 distribution system give the positive impacts such as reduce power losses (Sohi et al, 2011), improve voltage profile (Al- Abri et al, 2011), avoid generators congestion (Khanabadi et al, 2011), enhancement of network stability (Al Abri et al, 2013), reduce concentration of GHG (Sohi et al, 2011), enhancement of power quality (Sheng et al, 2011), reliability of overall subsystem in power system (Prabha et al, 2012) and save installation cost of new transmission as a response of load growth demand (Sheng et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Review of Considered Factors to Penetrate Renewable Energy Resources in Electrical Power System

Giyatno

2015

KEn

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ABSTRACT:As an increasing of load demand, scarcity of fossil fuel and penetration of greenhouse gasses (GHG) effect, utilization of renewable energy resources (RER) such as wind, solar, biomass and tidal are rising drastically. Distributed generation (DG) is a technology giving opportunity to integrate RER into power system network. These integrations are needed optimal long term planning. Those planning, hopefully, can increase reliability of electrical power system network while saving environment from GHG with minimum infestation, operation and maintenance cost. The aim of this paper is reviewing factors should be consider when preparing, operating and evaluating electrical power system with integration of RER. By this planning, it is expected that its integration is effective and efficient in a lifetime of project. Finally, this review can help government, researcher, engineer and private sector to make policies to preparing hybrid power system-DGs.

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Determining the optimal location and sizing of distributed generation Unit using Particle Swarm Optimization algorithm

Cited by 17 publications

References 7 publications

Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement

Optimal utilization of automated distributed generation in smart grid using genetic algorithm

A Review of Considered Factors to Penetrate Renewable Energy Resources in Electrical Power System

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