With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics such as load swings and load curtailment and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street dataset to demonstrate the validity of the proposed approach on real data.
翻译:随着分布式能源资源(DERs)在电网边缘(包括可再生能源发电、灵活负荷和储存)的日益渗透,在消费者一级准确预测分布式生产和消费变得十分重要,然而,由于隐私问题,基于反复或大量传输客户一级数据的DER预测是不可行的,在本文中,建议采用分布式机器学习方法,即Federal Learning,利用IoT节点网络进行DER预测,每个网络都传送消费和生成模式模型,而不透露消费者数据。我们考虑进行模拟研究,其中包括1000个DERs,并表明我们的方法导致准确预测维护消费者隐私,同时仍然导致准确预测。我们还评估了特定电网的性能指标,如工作量波动和减载等,并表明我们的FL算法能够令人满意地表现。我们还在Pecan街数据集进行模拟,以证明拟议方法对真实数据的有效性。