电力工程技术2026，Vol.45Issue(5)：27-39,13.DOI:10.12158/j.2096-3203.2026.05.003
基于深度学习加速的电力系统分层分布式多目标优化调度
Hierarchical distributed multi-objective optimization dispatching for power systems based on deep learning acceleration
摘要
Abstract
With the increasing scale of the power system,the existing distributed optimization dispatching method has the defects of slow calculation speed and poor optimization effect.To address this issue,a hierarchical distributed acceleration optimization method is proposed.Firstly,considering the objective function and constraints of economic dispatch in the power system,a hierarchical distributed optimization model is constructed.Then,based on the multi-objective optimization algorithm,a self-attention mechanism deep neural network(SADNN)is introduced and combined with a hierarchical distributed optimization model to propose a hierarchical distributed multi-objective mantis accelerated search algorithm based on SADNN(SADNN-HDMOMASA).This algorithm is used to improve the efficiency of economic dispatch in the power system and accelerate the calculation process of the entire system.Finally,the proposed algorithm is analyzed in simulation examples.The results show that:in the IEEE 118-bus system,the method proposed in this paper reduces carbon emissions by 1.13%,cost by 3.97%,and total system running time by 34.29 s compared to the hierarchical distributed optimization method.In the IEEE 2208-bus system,compared with the hierarchical distributed optimization method,the method proposed in this paper saves 1.13%of costs and 10.14%of carbon emissions,and improves computing speed by 23%.The proposed algorithm can effectively save the system power generation cost and carbon emissions while improving computing speed.关键词
分层分布式优化/多目标优化算法/深度神经网络(DNN)/运行时间/成本与碳排放/计算速度Key words
hierarchical distributed optimization/multi-objective optimization algorithm/deep neural network(DNN)/running time/cost and carbon emissions/computing speed分类
信息技术与安全科学引用本文复制引用
殷林飞,叶泳孜,张孝顺..基于深度学习加速的电力系统分层分布式多目标优化调度[J].电力工程技术,2026,45(5):27-39,13.基金项目
国家自然科学基金资助项目(62463001) （62463001）
