An energy optimal schedule method for distribution network considering the access of distributed generation and energy storage

• Published in: IET generation, transmission & distribution transmission & distribution, 2023-07, Vol.17 (13), p.2996-3015
• Main Authors: Liu, Keyan, Sheng, Wanxing, Li, Zhao, Liu, Fang, Liu, Qianyi, Huang, Yucong, Li, Yong
• Format: Article
• Language: English
• Subjects: distributed power generation; distribution networks; energy management systems; load forecasting
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/gtd2.12855

The access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG. An integrated load forecasting model based on convolutional neural networks and bidirectional long‐short term memory neural network is proposed to get higher forecast accuracy of the load. And the improved sparrow search algorithmmulti‐objective sparrow search algorithm framework is proposed to solve the energy optimal schedule issue for distribution network considering the participation of source‐load‐storage aggregation groups.