<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>This paper first explores the basic process and characteristics of the intelligent algorithm, calculates its fitness function after setting and initializing the intelligent algorithm population, and iterates continuously to obtain a satisfactory optimal solution on the basis of the initialized stochastic solution. Then the optimization of the firefly algorithm is studied. After initializing the firefly population, the random attraction model and the probability factor are introduced to optimize the algorithm. Then, the power scheduling intelligent agent strategy is studied in depth, and the structure and operation process of the intelligent agent operation strategy is determined, as well as its application areas are studied. Finally, the effect of grid load forecasting by power dispatching intelligent agents is analyzed and compared before and after the application of intelligent agent operation strategy in the power system. In terms of grid load prediction accuracy, the actual and prediction errors are basically between 0.02-0.16, which is very close to the actual value. In terms of user satisfaction, the previous user satisfaction was basically 0.75-0.8, and the maximum satisfaction was basically increased to more than 0.9 after applying the intelligent agent operation strategy. The intelligent agent operation strategy based on an intelligent optimization algorithm can effectively dispatch the power system and improve user satisfaction.</p>
</abstract>

This paper first explores the basic process and characteristics of the intelligent algorithm, calculates its fitness function after setting and initializing the intelligent algorithm population, and iterates continuously to obtain a satisfactory optimal solution on the basis of the initialized stochastic solution. Then the optimization of the firefly algorithm is studied. After initializing the firefly population, the random attraction model and the probability factor are introduced to optimize the algorithm. Then, the power scheduling intelligent agent strategy is studied in depth, and the structure and operation process of the intelligent agent operation strategy is determined, as well as its application areas are studied. Finally, the effect of grid load forecasting by power dispatching intelligent agents is analyzed and compared before and after the application of intelligent agent operation strategy in the power system. In terms of grid load prediction accuracy, the actual and prediction errors are basically between 0.02-0.16, which is very close to the actual value. In terms of user satisfaction, the previous user satisfaction was basically 0.75-0.8, and the maximum satisfaction was basically increased to more than 0.9 after applying the intelligent agent operation strategy. The intelligent agent operation strategy based on an intelligent optimization algorithm can effectively dispatch the power system and improve user satisfaction.

Analysis of intelligent agent operation strategy of power system scheduling based on intelligent optimization algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first explores the basic process and characteristics of the intelligent algorithm, calculates its fitness function after setting and initializing...

Analysis of intelligent agent operation strategy of power system scheduling based on intelligent optimization algorithm

Publicado en línea: 30 sept 2023

Páginas: -

Recibido: 22 nov 2022

Aceptado: 10 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00409

Palabras clave
Intelligent optimization algorithm, Power system scheduling, Intelligent agent operation, Firefly algorithm, Load forecasting.

© 2023 Jian Zuo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of intelligent agent operation strategy of power system scheduling based on intelligent optimization algorithm

Publicado en línea: 30 sept 2023

Páginas: -

Recibido: 22 nov 2022

Aceptado: 10 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00409

Palabras claveIntelligent optimization algorithm, Power system scheduling, Intelligent agent operation, Firefly algorithm, Load forecasting.

© 2023 Jian Zuo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Intelligent optimization algorithm, Power system scheduling, Intelligent agent operation, Firefly algorithm, Load forecasting.