A combined probabilistic modeling of renewable generation and system load types to determine allowable DG penetration level in distribution networks

Summary Determining the penetration level of the distributed generation (DG) is an effective tool to site and size DGs in distribution networks. A multiobjective function of optimizing the DG penetration level, subject to constraints including short circuit capacity, voltage limits, transformer capa...

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Vydáno v:International transactions on electrical energy systems Ročník 29; číslo 1; s. e2696 - n/a
Hlavní autoři: Naghdi, Marzieh, Shafiyi, Mohammad‐Agha, Haghifam, Mahmoud‐Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken John Wiley & Sons, Inc 01.01.2019
Témata:
Compensators
Distributed generation
Electric potential
Energy sources
Fitness
improved bee algorithm
Irradiance
Load
Load distribution
load type
Modelling
Monte Carlo simulation
Multiple objective analysis
Networks
Optimization
Penetration
Power flow
Probabilistic models
real and reactive losses
Renewable energy
renewable energy resources
Renewable resources
Short circuits
Statistical analysis
Tap changers
Voltage stability
Wind speed
ISSN:2050-7038, 2050-7038
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Shrnutí:Summary Determining the penetration level of the distributed generation (DG) is an effective tool to site and size DGs in distribution networks. A multiobjective function of optimizing the DG penetration level, subject to constraints including short circuit capacity, voltage limits, transformer capacity, reverse power flow, and congestion of lines, is introduced to minimize the real and reactive losses and to ensure the voltage stability considering the variations of loads, different locations and power factors of DGs, and statistical nature of DG powers especially renewable energy resources. This optimization is performed based on a combined probabilistic modeling of the chronological behavior of wind speed, solar irradiance, and load with different types. The dynamical behavior of the compensators and on‐load tap changers is modeled by optimization subfunctions. An improved bee algorithm is used through conducting searches in the neighboring areas based on a new nonlinear function for a higher fitness and coherency speed.
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ISSN:2050-7038
2050-7038
DOI:10.1002/etep.2696