Application of machine learning algorithm to optimize power grid transmission pricing and its economic evaluation model

Weihong Cao; Yuankun Jiang; Jie Yang

doi:10.1117/12.3054858

11 December 2024 Application of machine learning algorithm to optimize power grid transmission pricing and its economic evaluation model

Weihong Cao, Yuankun Jiang, Jie Yang

Author Affiliations +

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 1344536 (2024) https://doi.org/10.1117/12.3054858
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

This study optimizes power grid transmission prices using machine learning algorithms and then proceeds to work on economic evaluations to explore its application potential. The operation data of a provincial power system from 2019 to 2023 were obtained in the study, and many machine learning algorithms, such as linear regression, decision tree, random forest, and support vector machine, were used for model building and analysis. The results indicate that the proposed machine learning-based pricing strategy outperforms the current company pricing strategy, realized particularly in a 10% increase in revenue, an 8.3% reduction in costs, an 83.3% growth in profit, and 8.2% and 2.1% up in user satisfaction and energy efficiency levels, respectively. The comprehensive evaluation scores of the optimization strategy are worked out through this multidimensional economic evaluation index system, using the hierarchical analysis method, to verify the enormous advantages in economic benefits and operation efficiency.

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

Citation Download Citation

Weihong Cao, Yuankun Jiang, and Jie Yang "Application of machine learning algorithm to optimize power grid transmission pricing and its economic evaluation model", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 1344536 (11 December 2024); https://doi.org/10.1117/12.3054858

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