综合智慧能源2025，Vol.47Issue(11)：14-23,10.DOI:10.3969/j.issn.2097-0706.2025.11.002
基于自适应多任务扩散模型的风光荷场景生成方法
Wind-solar-load scenario generation method based on adaptive multi-task diffusion model
摘要
Abstract
In the context of high-penetration renewable energy integration,accurately constructing wind-solar-load joint scenarios that can capture the dynamic characteristics and complex correlations of various variables has emerged as a core requirement for power system scheduling and control.To this end,a generation method for wind-solar-load scenarios based on an adaptive multi-task diffusion model was proposed.A multi-task diffusion model learning architecture based on a joint denoising network was established.By jointly processing multivariate state vectors and integrating temporal information,realistic joint scenarios that integrated physical coupling relationships and temporal dependency patterns were generated.On this basis,an adaptive diffusion strategy module guided by the dynamic features of heterogeneous data was proposed.Dynamic statistical features of the generated data were extracted,and the noise scheduling of the diffusion process was dynamically adjusted accordingly,thereby effectively characterizing the non-stationary and time-varying dynamic characteristics of the data.Meanwhile,a training criterion guided by structured consistency was introduced,where the marginal distribution and joint dependency characteristics of the data structure were constrained within the training objectives.It effectively guided the model generation process and improved the quality of wind-solar-load scenario generation.Validation based on the IES-134 standard dataset demonstrated that the proposed model could effectively generate wind-solar-load joint scenarios with realistic physical characteristics and reasonable statistical patterns,providing a practical tool for optimal scheduling and risk assessment in power systems.关键词
可再生能源/风光荷联合场景/自适应多任务扩散模型/去噪扩散概率模型/多任务学习/动态特性自适应Key words
renewable energy/wind-solar-load joint scenarios/adaptive multi-task diffusion model/denoising diffusion probabilistic model/multi-task learning/dynamic feature adaptation分类
能源科技引用本文复制引用
张志洪,胡旭光,吴恩凯,张弛,周诚浩..基于自适应多任务扩散模型的风光荷场景生成方法[J].综合智慧能源,2025,47(11):14-23,10.基金项目
国家自然科学基金项目(62303103,62373089) （62303103,62373089）
中央高校基本科研业务费专项资金项目(N25ZJL020) （N25ZJL020）
辽宁省自然科学基金项目(2023-BSBA-140) （2023-BSBA-140）
辽宁省教育厅科研项目(JYTQN2023161)National Natural Science Foundation of China(62303103,62373089) （JYTQN2023161）
Fundamental Research Funds for the Central Universities in China(N25ZJL020) （N25ZJL020）
Natural Science Foundation of Liaoning Province(2023-BSBA-140) （2023-BSBA-140）
Scientific Research Project of Liaoning Provincial Department of Education(JYTQN2023161) （JYTQN2023161）
