Modified GA for optimal DG allocation and sizing

Babu, U. Ramesh; Reddy, V Vijay Kumar; Tarakalyani, S.

doi:10.1109/eesco.2015.7253775

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The conventional DN planning could be categorised into substation planning, network planning and substation‐network co‐optimisation [1]. Currently, researchers gradually put their attentions on integrating DG allocation planning [2–8], ESS optimisation [9–11], electrical vehicle charging station planning [12] and active management strategy [13] to minimise the cost or improve the operation performance, in which various heuristic methods are applied in majority. Ganguly and Samajpati [3] proposed a model to minimise network power loss and node voltage deviation.…”

Section: Introductionmentioning

confidence: 99%

Distribution system planning incorporating distributed generation and cyber system vulnerability

Honghao

Shi

2017

J. eng.

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Section: Introductionmentioning

confidence: 99%

Distribution system planning incorporating distributed generation and cyber system vulnerability

Honghao

Shi

2017

J. eng.

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“…The location and sizing of DG can be obtained through genetic algorithm and optimal power flow technique [5]. Further, a modified genetic algorithm is suggested in [6] to determine optimal DG units sizing and location.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Real Power Loss and Voltage Stability Index for Distribution Systems with Distributed Generation

Elshahed

Dawod²,

Osman

2017

JERA

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Integrating Distributed Generation (DG) units into distribution systems can have an impact on the voltage profile, power flow, power losses, and voltage stability. In this paper, a new methodology for DG location and sizing are developed to minimize system losses and maximize voltage stability index (VSI). A proper allocation of DG has to be determined using the fuzzy ranking method to verify best compromised solutions and achieve maximum benefits. Synchronous machines are utilized and its power factor is optimally determined via genetic optimization to inject reactive power to decrease system losses and improve voltage profile and VSI. The Augmented Lagrangian Genetic Algorithm with nonlinear mixed-integer variables and Non-dominated Sorting Genetic Algorithm have been implemented to solve both single/multi-objective function optimization problems. For proposed methodology effectiveness verification, it is tested on 33-bus and 69-bus radial distribution systems then compared with previous works.

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Fast assessment of photovoltaic power absorptive capacity in distribution line

Zhou

Zhang

et al. 2016

2016 IEEE International Conference on Power System Technology (POWERCON)

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Modified GA for optimal DG allocation and sizing

Cited by 3 publications

References 10 publications

Distribution system planning incorporating distributed generation and cyber system vulnerability

Distribution system planning incorporating distributed generation and cyber system vulnerability

Optimization of Real Power Loss and Voltage Stability Index for Distribution Systems with Distributed Generation

Fast assessment of photovoltaic power absorptive capacity in distribution line

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