Convex probabilistic allocation of wind generation in smart distribution networks

This study introduces a probabilistic optimisation model for allocation of renewable distributed generations (DGs) in radial distribution networks. The methodology is based on a probabilistic generation – load model that combines all possible operating conditions of the wind-based DG units as well a...

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Vydané v:IET renewable power generation Ročník 11; číslo 9; s. 1211 - 1218
Hlavní autori: Akbari, Mohammad-Amin, Aghaei, Jamshid, Barani, Mostafa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 12.07.2017
Predmet:
adaptive power factor control
computational complexity
computationally NP‐hard problem
convex probabilistic allocation
distributed power generation
distribution networks
energy losses reduction
IEEE 33‐bus radial distribution systems
IEEE 4‐bus radial distribution systems
integer programming
load flow
load tap changer control
MINLP
mixed‐integer nonlinear programming
multiobjective performance index
nonlinear programming
on load tap changers
optimal allocation
optimisation
power factor
probabilistic AC optimal power flow
probabilistic generation
probabilistic optimisation model
radial distribution networks
renewable distributed generation allocation
Research Article
second‐order cone programming problem
smart distribution networks
smart grids
smart power grids
voltage improvement
wind generation
wind power
wind‐based DG units
radial distribution networks
IEEE 33-bus radial distribution systems
smart distribution networks
renewable distributed generation allocation
distribution networks
nonlinear programming
integer programming
wind generation
probabilistic AC optimal power flow
smart power grids
distributed power generation
wind-based DG units
probabilistic generation
optimisation
smart grids
load tap changer control
energy losses reduction
adaptive power factor control
multiobjective performance index
voltage improvement
IEEE 4-bus radial distribution systems
on load tap changers
MINLP
power factor
probabilistic optimisation model
mixed-integer nonlinear programming
second-order cone programming problem
computationally NP-hard problem
wind power
convex probabilistic allocation
optimal allocation
load flow
computational complexity
ISSN:1752-1416, 1752-1424, 1752-1424
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Popis
Shrnutí:This study introduces a probabilistic optimisation model for allocation of renewable distributed generations (DGs) in radial distribution networks. The methodology is based on a probabilistic generation – load model that combines all possible operating conditions of the wind-based DG units as well as load levels with their probabilities. A multiobjective performance index is extracted that is formulated as a combination of two indices, namely energy losses reduction and voltage improvement. Besides, a probabilistic AC optimal power flow is used to determine the optimal allocation of wind DG and maximise the multiobjective performance index. Two alternative control approaches of the future smart grids, i.e. area based under load tap changer control and adaptive power factor control, are assessed to maximise potential benefits and expand the penetration level of DGs. At first, this problem is formulated as a mixed-integer non-linear programming (MINLP) which leads to a computationally NP-hard problem. Accordingly, the obtained MINLP problem is relaxed and reformulated in the form of a well-suited second-order cone programming problem which is computationally efficient scheme to be solved. The implementation of the proposed framework on 4-bus and IEEE 33-bus radial distribution systems shows the performance of the proposed optimisation mechanism.
ISSN:1752-1416
1752-1424
1752-1424
DOI:10.1049/iet-rpg.2017.0100