Study of Dynamic Economic Load Dispatch Using Particle Swarm Optimization with Multi-information Characteristics

Authors

Huang Song, 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;;
Wang Yan, 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;;
Zhicheng Ji, 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;;2. Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: According to the characteristics of power system, an model of dynamic economic load dispatch based on multiple fuel resources and 24-hour forecasting load demand was established, and a particle swarm optimizer using multi-information characteristics of all personal-best information was proposed to solve it. In the algorithm, centroid position and median position were defined, and then the personal-best position, centroid position and median position was developed to modify the velocity update formula. The algorithm could reduce the premature phenomenon for solving complex optimization problems, and it could make effectively solve dynamic economic load dispatch. The simulation was conducted to optimize DED1 instance, and the results show that the algorithm can effectively solve the problem and has a better performance.

Recommended Citation

Song, Huang; Yan, Wang; and Ji, Zhicheng (2020) "Study of Dynamic Economic Load Dispatch Using Particle Swarm Optimization with Multi-information Characteristics," Journal of System Simulation: Vol. 29: Iss. 9, Article 39.
DOI: 10.16182/j.issn1004731x.joss.201709038
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss9/39

First Page

2159

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201709038

Last Page

2167

CLC

TP18;TM85

Recommended Citation

Huang Song, Wang Yan, Ji Zhicheng. Study of Dynamic Economic Load Dispatch Using Particle Swarm Optimization with Multi-information Characteristics[J]. Journal of System Simulation, 2017, 29(9): 2159-2167.

DOI

10.16182/j.issn1004731x.joss.201709038