Optimization method of power grid economic dispatching based on adaptive particle swarm optimization algorithm

Shijing Dai; Zongliang Yang; Xingpei Chen; Yongze Wang

doi:10.1117/12.2684590

19 October 2023 Optimization method of power grid economic dispatching based on adaptive particle swarm optimization algorithm

Shijing Dai, Zongliang Yang, Xingpei Chen, Yongze Wang

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093K (2023) https://doi.org/10.1117/12.2684590
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Some grid economic dispatch optimization methods have the problem of high consumption costs. To improve this shortcoming, an adaptive particle swarm algorithm-based grid economic dispatch optimization method is designed. Obtaining grid operating costs, operate in maximum power point tracking mode, adopt full grid acquisition, calculate daily fixed investment costs for the grid, construct a two-level planning mathematical model, adjust battery output and design an economic dispatch optimisation method based on an adaptive particle swarm algorithm. Experimental results: The mean values of maintenance cost, fuel cost and environmental management cost of the grid economic dispatch optimisation method in the paper are: RMB 49.193, RMB 104.488 and RMB 310.045 respectively, indicating that the designed grid economic dispatch optimisation method is more effective after making full use of the adaptive particle swarm algorithm.

Citation Download Citation

Shijing Dai, Zongliang Yang, Xingpei Chen, and Yongze Wang "Optimization method of power grid economic dispatching based on adaptive particle swarm optimization algorithm", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093K (19 October 2023); https://doi.org/10.1117/12.2684590

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available