Optimal Economic Dispatching in Distribution Network Involving Data Augmentation and Power Source-Load Uncertainties

Abstract

The uncertainties of wind power generation and load significantly impact on the economy of distribution network dispatching. Therefore, operating an optimal dispatching strategy can improve the economic performance of the power grid. In modern data-driven optimization approaches, the proper volume of data generates remarkably optimal results. However, due to the difficulties of data collection, transmission, and storage, wind power data is naturally scarce, which severely deteriorates the performance of the optimization. Additionally, the uncertainty existing on the load side leads to more complicated scenarios in optimization, which further prevents the optimization from achieving the optimal dispatching strategy. Therefore, to solve the aforementioned issues, this paper presents an economic dispatching optimization approach for a distribution network while considering wind power data augmentation and uncertainties of both wind power and load. Firstly, the synthetic minority over-sampling technique (SMOTE) is adopted to generate a proper number of wind power generation output data samples. And then, a variational autoencoder (VAE) is further employed to generate adequate data samples with a similar distribution to the original data samples. Secondly, the deep embedded clustering algorithm (DECA) deeply characterizes and clusters the augmented data samples to figure out typical wind power generation output scenarios overrepresented in the samples. Thirdly, for each typical scenario, an ambiguity set is constructed using an imprecise Dirichlet data-driven model (IDDM); And then, the details, including the objective functions and constraints of the economic dispatching for the distribution network are analyzed and modeled. Finally, a two-stage distributed robust optimization (DRO) model considering the uncertainties of both wind power generation output and load for achieving the optimal economic dispatching strategy for the distribution network is established and solved; The case studies demonstrate the effectiveness of the presented approach.