By Topic

Power flow and dynamic optimal power flow including wind farms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gonggui Chen ; Collage of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China ; Jinfu Chen ; Xianzhong Duan

With the increasing levels of wind generator penetration in modern power systems, one of major challenges in the present and coming years is the optimization control, such as optimal power flow including wind farms. The power flow model for a fixed speed wind generator (FSWG) system and a variable speed wind generator (VSWG) system is discussed respectively. The expectation model of wind generators' active power outputs is adopted. Dynamic optimal power flow (DOPF) is a typical complex multi-constrained non-convex non-linear programming problem in wind power integrated system when considering the valve-point effect of conventional generators. Moreover, it is mixed integer when considering the discreteness of FSWG reactive compensation devices. DOPF model is established in this paper, and then a novel shuffled frog leaping algorithm (SFLA) is proposed for solving the established DOPF model. According to the principle of the nearest reactive power compensation, the required reactive power of FSWG doesn't absorb from the system as much as possible, but mainly from its reactive compensation devices. Improved IEEE 30-bus system is used to illustrate the effectiveness of the proposed method compared with those obtained from particle swarm optimization (PSO) algorithm. The test results show that the proposed method is effective and has a certain practicality.

Published in:

2009 International Conference on Sustainable Power Generation and Supply

Date of Conference:

6-7 April 2009