DocumentCode :
157694
Title :
PAR/PST location and sizing in power grids with wind power uncertainty
Author :
Miranda, V. ; Alves, Renan
Author_Institution :
Fac. of Eng., Univ. of Porto, Porto, Portugal
fYear :
2014
fDate :
7-10 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
This paper presents a new stochastic programming model for PAR/PST definition and location in a network with a high penetration of wind power, with probabilistic representation, to maximize wind power penetration. It also presents a new optimization meta-heuristic, denoted DEEPSO, which is a variant of EPSO, the Evolutionary Particle Swarm Optimization method, borrowing the concept of rough gradient from Differential Evolution algorithms. A test case is solved in an IEEE test system. The performance of DEEPSO is shown to be superior to EPSO in this complex problem.
Keywords :
evolutionary computation; particle swarm optimisation; power grids; stochastic programming; wind power plants; IEEE test system; PAR-PST location; PAR-PST sizing; complex problem; denoted DEEPSO; differential evolution algorithm; evolutionary particle swarm optimization method; optimization meta-heuristic; power grids; stochastic programming model; wind power penetration; wind power uncertainty; Lead; Linear programming; Optimization; Sociology; Statistics; Stochastic processes; Wind power generation; Differential Evolution; Evolutionary Particle Swarm Optimization; PAR location; Wind power integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on
Conference_Location :
Durham
Type :
conf
DOI :
10.1109/PMAPS.2014.6960679
Filename :
6960679
Link To Document :
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