Journal of System Simulation
Abstract
Abstract: Because of the randomness and fluctuation of the wind power, the large scale wind power integration makes the economic emission dispatch of power systems more complicated. By fusing the advantages of the differential evolution algorithm with the parameter self-adaption and crisscross optimization, a hybrid intelligent optimization algorithm called ADE-CSO is proposed to solve the dynamic economic emission dispatch considering wind power integration. A constraint handling technology is introduced to satisfy the feasibility of the power balance and ramp limits. To demonstrate the effectiveness of the proposed algorithm, a typical test case of five generator bus system is conducted and compared with four other intelligent optimization algorithms. The experiment results show that the proposed ADE-CSO has good optimization performance and global convergence ability, and is an effective algorithm to the dynamic economic emission dispatch.
Recommended Citation
Mei, Panpan; Wu, Lianghong; Zhang, Hongqiang; and Wang, Huiying
(2020)
"Adaptive Differential Crisscross Optimization Algorithm for Dynamic Economic Emission Dispatch Considering Wind Power,"
Journal of System Simulation: Vol. 32:
Iss.
6, Article 21.
DOI: 10.16182/j.issn1004731x.joss.18-0647
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss6/21
First Page
1179
Revised Date
2019-02-23
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.18-0647
Last Page
1187
CLC
TP13
Recommended Citation
Mei Panpan, Wu Lianghong, Zhang Hongqiang, Wang Huiying. Adaptive Differential Crisscross Optimization Algorithm for Dynamic Economic Emission Dispatch Considering Wind Power[J]. Journal of System Simulation, 2020, 32(6): 1179-1187.
DOI
10.16182/j.issn1004731x.joss.18-0647
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons