Distributed generation allocation based on average daily load and power production curves

Prenc, Rene; Škrlec, Davor; Komen, Vitomir

doi:10.1016/j.ijepes.2013.05.033

Cited by 55 publications

(23 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The daily load schedule for the distribution network has been modeled for 24 h duration of the day and it is repeated for 365 days for obtaining the annual load schedule [15,17]. At each load level considered, the total load of the distribution network is a fraction of the peak load.…”

Section: Resultsmentioning

confidence: 99%

“…Shaaban et al [16] have proposed a GA based multiobjective approach for optimal allocation of different types of DG units to minimize investment costs of system upgradation, cost of annual energy losses and to improve the reliability of the system. Prenc et al [17] have also studied the DG power penetration based on average daily load and power production curves but in this method they have not considered any optimization technique. Kansal et al [18] have used particle swarm optimization technique for the placement of DGs in a distribution network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum placement and sizing of DGs considering average hourly variations of load

Gampa

Das

2015

International Journal of Electrical Power & Energy Systems

158

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum placement and sizing of DGs considering average hourly variations of load

Gampa

Das

2015

International Journal of Electrical Power & Energy Systems

158

View full text Add to dashboard Cite

“…Compared to huge main power plants based on fossil fuel, oil, along with gas-fired plants [3], DGs resources for instance wind generators, photovoltaic, fuel cells, biomass, micro generators, small hydroelectric plant, landfill gas, and so on. [4] get a reduced amount of funds expenditure along with maintenance and upkeep costs, the area is a lot easier to get and possess less unfavorable affect the surroundings [5]. The important thing advantages which is available from DGs tend to be advancement associated with voltage stability & voltage profile, power loss lessening, superior system trustworthiness & safety along with improved power quality (PQ) & service associated with additional solutions [6][7].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Durairasan¹,

Kalaiselvan²,

Sait³

2017

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem

show abstract

“…In [3] also, almost same objective is tackled in the presence of time varying load by a novel approach based on dynamic programming method. The same GA optimization technique is used by Prenc et al to discuss the optimal allocation of three different types of distributed generation units like solar parks, wind farms and power stations (which are independent on an intermittent primary energy source) with a goal of minimizing cumulative average daily active power losses [4]. A comparative study of different methods such as novel method, combined power loss sensitivity method, index vector method and voltage sensitivity index method have been done in [5] for finding optimal location and sizing of distributed generation (DG) in a distribution network.…”

Section: Introductionmentioning

confidence: 99%

Comparative performance study of various PSO techniques for multiple allocations of DG units in radial distribution system

Kumar

Mandal

Chakraborty

2015

2015 IEEE Power, Communication and Information Technology Conference (PCITC)

View full text Add to dashboard Cite

The concept of Distributed Generation (DG) emerged in the power industry from the last few decades due to the rapid increase in electricity demand, limited conventional resources and continuous growth in nonconventional energy based low voltage and small scale power industry. In distributed generation system, the optimal placement and sizing of DG units in radial distribution network is one of the most essential conditions to minimize the total active power loss and to improve the voltage profile. In this paper, different types of improved version of Particle Swarm Optimization (PSO) techniques are applied to solve the aforesaid optimization problem. Due to self organizing nature and time dependent strategic variation in acceleration coefficients of Self-organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients (HPSO-TVAC) technique, this technique performs better in most of the cases for both IEEE-33 bus and IEEE-69 bus system. Here, IEEE-33 bus and IEEE-69 bus radial distribution system is taken for the analysis and for testing the superiority of these techniques.

show abstract

Distributed generation allocation based on average daily load and power production curves

Cited by 55 publications

References 7 publications

Optimum placement and sizing of DGs considering average hourly variations of load

Optimum placement and sizing of DGs considering average hourly variations of load

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Comparative performance study of various PSO techniques for multiple allocations of DG units in radial distribution system

Contact Info

Product

Resources

About