| 注册
首页|期刊导航|综合智慧能源|基于NSMFO-BERT算法的电力系统多目标优化经济调度研究

基于NSMFO-BERT算法的电力系统多目标优化经济调度研究

曾浩政 殷林飞

综合智慧能源2025,Vol.47Issue(4):98-106,9.
综合智慧能源2025,Vol.47Issue(4):98-106,9.DOI:10.3969/j.issn.2097-0706.2025.04.008

基于NSMFO-BERT算法的电力系统多目标优化经济调度研究

Research on multi-objective optimal economic dispatch of power systems based on NSMFO-BERT algorithm

曾浩政 1殷林飞1

作者信息

  • 1. 广西大学 电气工程学院,南宁 530004
  • 折叠

摘要

Abstract

With the increasing integration of renewable energy,traditional power system models can no longer meet the complex demands of modern power systems.To adapt to the trend of multi-energy collaborative power generation,a new-type power system model was developed,primarily based on thermal power generation with renewable energy as supplementary sources.Due to the multi-objective trade-offs between power generation costs and carbon emission targets in the new-type power system,an intelligent optimization method was required to dynamically adjust the output of each generating unit and fully leverage the advantages of various energy sources.Therefore,a non-dominated sorting moth-flame optimization algorithm based on bidirectional encoder representations from transformers(NSMFO-BERT)was proposed.As a large model,BERT excelled in handling complex data relationships.By learning from NSMFO,it established the relationship between the active power of generating units and load forecasting,rapidly developing scheduling strategies for a large number of generating units.Simulation results showed that compared to NSMFO,the multi-objective grey wolf algorithm,and the multi-objective ant lion algorithm,NSMFO-BERT could find a Pareto curve with lower target values for power generation costs and carbon emissions.Furthermore,the computation speed of the proposed algorithm was 69.3%,61.4%,and 90.9%faster than the aforementioned algorithms,respectively.It demonstrated strong generalization ability,suitable for addressing large-scale power system scheduling problems.

关键词

双向编码器表示转换器/非支配飞蛾扑火优化算法/大模型/新型电力系统/发电成本/碳排放量

Key words

bidirectional encoder representations from transformers/non-dominated sorting moth-flame optimization algorithm/large model/new-type power system/power generation cost/carbon emissions

分类

能源科技

引用本文复制引用

曾浩政,殷林飞..基于NSMFO-BERT算法的电力系统多目标优化经济调度研究[J].综合智慧能源,2025,47(4):98-106,9.

基金项目

国家自然科学基金项目(62463001) National Natural Science Foundation of China(62463001) (62463001)

综合智慧能源

2097-0706

访问量0
|
下载量0
段落导航相关论文