中国电机工程学报2025，Vol.45Issue(20)：7987-7996,中插12,11.DOI:10.13334/j.0258-8013.pcsee.240990
基于寻优轨迹学习的优化方法及其在区域综合能源系统海量计算中的应用
Optimization Method Based on Learning From Trajectories and Its Application in DIES Massive Settlement
摘要
Abstract
Optimization methods typically search for a descent direction based on the current solution.However,they do not acquire any prior knowledge that could be used to solve new problems in the future,resulting in reduced efficiency over time.This paper takes the computationally intensive problem of market value distribution and settlement for district integrated energy systems(DIES)as an example to address this issue.A method is proposed that utilizes historical information obtained during this optimization process for cognitive learning to update prior knowledge.Based on the current prior knowledge,values of current variables are judged,which can help accelerate the optimization of variable-structure subproblems.As the master problem is being solved,the probability of making a variable basis is consistently updated by learning from reduced-cost offset.In the variable-structure sub-problems,the scale of the basic variables is reduced based on this probability.Additionally,the entering basis is further selected from the basic variables with the largest decrease in space according to prior knowledge,which leads to a more effective pivoting and a quicker solution to the variable structure problems.The case study of multi-agent DIES variable-structure optimization indicates that compared to direct optimization,this method can reduce the solution time to an order of 10-1～10-3 seconds.By a robustness test and running the case on a GPU,the effectiveness,robustness,and scalability of the proposed method have been verified.关键词
电力市场机制/价值分配/最优基置换/先验知识/寻优轨迹学习Key words
power market mechanism/value distribution/optimal basis change/prior knowledge/optimization trajectory learning分类
信息技术与安全科学引用本文复制引用
朱灏翔,钟海旺,康重庆,夏清,赖晓文..基于寻优轨迹学习的优化方法及其在区域综合能源系统海量计算中的应用[J].中国电机工程学报,2025,45(20):7987-7996,中插12,11.基金项目
国家重点研发计划项目(2022YFB2403500) （2022YFB2403500）
国家优秀青年科学基金项目(52122706).National Key R&D Program of China(2022YFB2403500) （52122706）
Outstanding Youth Science Foundation of China(52122706). （52122706）
