Optimal DG placement and capacity allocation using intelligent algorithms

Alinezhad, Parvin; Bakhoda, Omid Zhoulai; Menhaj, Mohammad Bagher

doi:10.1109/cfis.2015.7391657

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Intelligent optimization algorithms mainly include a genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, evolutionary algorithm, etc. [64][65][66][67]. Table 1 lists the intelligent optimization algorithms used for the capacity optimization of the multi-energy hybrid power system for ships.…”

Section: System Integration and Capacity Optimizationmentioning

confidence: 99%

A review of multi-energy hybrid power system for ships

Yuan

Wang

Yan

et al. 2020

Renewable and Sustainable Energy Reviews

126

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Section: System Integration and Capacity Optimizationmentioning

confidence: 99%

A review of multi-energy hybrid power system for ships

Yuan

Wang

Yan

et al. 2020

Renewable and Sustainable Energy Reviews

126

View full text Add to dashboard Cite

“…Furthermore, there is more optimization technique used in the optimal placement of DG. In [8], the researcher has investigated to reduce operation cost and decide the capacity and location of DGs in the grid by various optimization techniques. GSA has discovered better answers with fewer cost, although it used more time to stimulate the results.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Load Variation Consideration for Optimal Distributed Generation Placement

Kadir¹,

Fani²,

Ropidah³

et al. 2021

IJACSA

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Distributed generation (DG) devices offered usefully for power losses minimization, grid reinforcement, bus voltages improvement and efficiency of a distribution system. Usually, the DG placement problem considers the predefined DG number and sizes that might result in many small DGs. However, a better solution could be reached with a minimum number of DGs, reducing installation and maintenance costs. Furthermore, the increment of load and vice versa may affect the voltage profile below or upper the tolerable limit and distribution feeders. Thus, this paper aims to analyze the impact of the variation of the load level with the DG connection in the power system by using the improved gravitational search algorithm (IGSA) as an optimization technique. The multi-objective function target reduces the total power loss, average total voltage harmonic distortion and voltage deviation in the distribution system. This study is considering six different load levels as in percentage of load. This proposed technique compares with the particle swarm optimization (PSO) and the gravitational search algorithm (GSA). This efficiency of the proposed technique tests on the 33-bus radial distribution system with six case studies.

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“…Kefayat et al, presented a hybrid of ant colony optimization and artificial bee colony optimization for optimal siting and sizing of DG [5]. Alinezhad et al, proposed three algorithms, PSO, GSA and GA for optimal location of DG in distribution system [6]. Jordehi proposed various approaches for determination the best sitting and sizing of DGs in electric power systems [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of RDGs in electrical distribution networks using hybrid SAPSO algorithm

Ali

Mehanna

Othman

2020

IJECE

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The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA & PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA & PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile.

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Optimal DG placement and capacity allocation using intelligent algorithms

Cited by 10 publications

References 11 publications

A review of multi-energy hybrid power system for ships

A review of multi-energy hybrid power system for ships

Analysis of Load Variation Consideration for Optimal Distributed Generation Placement

Optimal planning of RDGs in electrical distribution networks using hybrid SAPSO algorithm

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