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