准确刻画风光时序功率曲线对于加快推动新能源大规模并网、指导联合发电系统规划运行具有重要意义.针对我国沙漠、戈壁、荒漠等地区新建大型风电光伏发电基地无历史功率数据可利用的现状,该文提出基于改进域对抗网络(improved domain adversarial neural networks,IDANN)的新能源基地风光时序功率曲线生成方法.以历史气象和功率数据充足的新能源场站作为源域,仅有气象数据的新建基地作为目标域.将源域上学习的输入气象信息到输出风光功率的非线性映射知识迁移到目标域,并添加最大均值差异(maximum mean discrepancy,MMD)作为度量域间特征分布相似性的损失函数以降低目标域泛化误差.最后采用实际风光场站算例验证所提方法的有效性,并进一步表明该方法的实用价值和意义.
Improved Domain Adversarial Neural Networks Based Generation Method of Wind-photovoltaic Power Time Series Curves for Renewable Energy Base
Accurately characterizing the time series power curves of wind-photovoltaic is crucial for accelerating renewable energy development and guiding the operation and security of integrated energy systems.In response to the current situation of new large-scale wind and photovoltaic bases in the desert,Gobi,and barren areas of China without available historical power data,this paper proposes a method for generating wind-photovoltaic power curves using an improved domain adversarial neural network.Firstly,Existing stations with sufficient historical weather and power data are considered as the source domain,and newly established bases with only weather data are considered as the target domain.Then,we transfer the knowledge of the non-linear mapping from input weather information to output wind-photovoltaic power learned in the source domain to the target domain and add maximum mean discrepancy as a loss function to measure the similarity of feature distributions between domains for reducing the generalization error in the target domain.Finally,the effectiveness of the proposed method is verified by actual wind-photovoltaic power station examples,and the practical value and significance of the method is further demonstrated.
wind-photovoltaic power time seriesimproved domain adversarial neural networksGobi Desert and other arid regionstransfer learning
万方数据
维普
