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An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems
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 Title & Authors
An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems
Kim, Min Jeong; Song, Hyoung-Yong; Park, Jong-Bae; Roh, Jae-Hyung; Lee, Sang Un; Son, Sung-Yong;
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 Abstract
This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a `Mean-Variance Optimization (MVO)` algorithm with Kuhn-Tucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.
 Keywords
Nonconvex optimization;Economic dispatch;Mean-variance optimization;Kuhn-Tucker condition;
 Language
English
 Cited by
1.
Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer, Neural Computing and Applications, 2016, 27, 5, 1301  crossref(new windwow)
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