PSO-based optimal active-reactive power dispatch (OARPD) with wind power penetration

Pao Yue-kong Library Electronic Theses Database

PSO-based optimal active-reactive power dispatch (OARPD) with wind power penetration

 

Author: Xu, Xu
Title: PSO-based optimal active-reactive power dispatch (OARPD) with wind power penetration
Degree: M.Sc.
Year: 2016
Subject: Hong Kong Polytechnic University -- Dissertations
Wind power.
Electric power systems -- Mathematical models.
Department: Faculty of Engineering
Pages: viii, 51 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2952759
URI: http://theses.lib.polyu.edu.hk/handle/200/8896
Abstract: With the growing demand for renewable energy, wind energy is receiving more and more attention as the clean, non-polluting new renewable energy. As a kind of renewable and clean energy, wind power is of great significance for improving the energy structure, tackling climate change and solving energy security problems. Wind power has been the fastest growing forms of electricity generation because it has low fuel costs, less environmental pollution, etc., so it plays an indispensable role in the energy structure. However, the volatility, intermittence, and uncontrollability of wind energy bring great challenges to the stable operation and economical scheduling of the power system with large-scale wind power connected [1]. For reasonable planning and dispatching of the power system, an in-depth study on the effect of large-scale integrated wind power to the reliability and economic dispatch of the power system is important in theoretical meanings and practical engineering. Volatility and randomness of the large-scale wind power integration bring some problems to the dynamic economic dispatch, which have attracted a large number of talented people to carry out a variety of researches [2]. Under the good prospects and efforts of researchers, by now there are many solving methods which can be divided into two categories--traditional algorithms and artificial intelligence algorithms. The former one includes dynamic programming method, prioritization method and Lagrangian relaxation. With the advancement of science and the development of computer technology, a series of artificial intelligence algorithms including genetic algorithm, simulated annealing algorithm and particle swarm algorithm are widely used, which has been successfully applied to the power system economic dispatch problem. This type of algorithm has better global search capability than the traditional one, using random search strategy so that optimization can be applied in the entire space [3]. However, it takes more time because it is less sensitive to specific issues and models. It also can be changed to a great number of functions for a wide range of applications, so it has higher practical value.
In this paper, the particle swarm optimization algorithm is used and two strategies are used to improve computing speed and efficiency. Firstly, the basic particle swarm optimization algorithm (bPSO) is used to generate feasible solutions, and then, due to the fact that bPSO has some demerits such as easily relapsing into local extremum, low convergence velocity and precision in the late evolutionary, adding extreme value perturbation operator as improvement strategy to overcome the demerits of the bPSO and inducing another algorithm--the extremum and simple particle swarm optimization (tsPSO) to optimize the feasible solutions. At last, based on an example of economic dispatch considering grid-connected wind power using the IEEE-57 bus test system, MATLAB is used to demonstrate this method. Results of this example are analyzed, which show that the strategy presented in this paper has good stability, fast searching speed, satisfactory optimization result, high efficient search capability and adaptability. The experiments prove that tsPSO can effectively overstep the local extremum, making PSO more practical.

Files in this item

Files Size Format
b29527594.pdf 1.462Mb PDF
Copyright Undertaking
As a bona fide Library user, I declare that:
  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

     

Quick Search

Browse

More Information