Clustered multi-node learning of electric vehicle charging flexibility

Forecasting the available flexible load provided by electric vehicles would enable electric utilities to make informed decision in utilizing these loads for enhancing the operational efficiency of distribution systems. To overcome the lack of historical loads data at newly-installed EV charging stations, this paper proposes a clustered multi-node learning with Gaussian Process (CMNL-GP) method to fuse data from multiple charging stations and to learn them simultaneously. The proposed method improves the forecasting accuracy in each node by transferring meaningful information among multiple nodes. The proposed method also performs a clustering algorithm within its objective function to obtain within-cluster similarity, since all the nodes may not be related equally, and the nodes within a cluster may have a stronger correlation. To characterize the clustered structures and to transfer the shared information among multiple nodes, different regularization terms are imposed in the objective function of the proposed method. The proposed clustered multi-node learning also utilizes the Gaussian Process for statistical attributes of the residual stochastic process, which refers to the information that may not be shared among multiple nodes and can be node-specific. The proposed method is validated by real-world EV charging stations data in State of Utah, USA, to demonstrate the effectiveness of the proposed algorithm. •A novel method proposed for flexible load forecasting represented by EVs•Flexible load data from similar EV charging stations are learned jointly•The existing multi-task learning algorithm is improved for time-series data•Clear discussions and guidelines for implementing of the proposed method are provided