High load balancing scheduling method of power system based on multi-objective genetic algorithm

Rui Zhang; Xing Wang

doi:10.1117/12.3030146

5 June 2024 High load balancing scheduling method of power system based on multi-objective genetic algorithm

Rui Zhang, Xing Wang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131633G (2024) https://doi.org/10.1117/12.3030146
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In order to continuously and stably provide enough electric energy to users under high load conditions. This paper presents a high load balanced scheduling method for power system based on multi-objective genetic algorithm. Calculate power system transmission link load. Aiming at the defects of genetic algorithm, it is improved to obtain multi-objective genetic algorithm. Combined with two peak-avoiding scheduling methods, this algorithm is used to achieve balanced scheduling of high loads. The experimental results show that: After the application of the research method, the power load fluctuation in this period is more balanced, the load balance is the highest, and the time is shorter.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Zhang and Xing Wang "High load balancing scheduling method of power system based on multi-objective genetic algorithm", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131633G (5 June 2024); https://doi.org/10.1117/12.3030146

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