The contents of power flow calculation and analysis of novel distribution system with high penetration of distributed generators (DGs) are state-of-the-art, abstract and difficult to learn. In order to improve the teaching effect and enhance the electrical engineering students’ understanding of novel power distribution system, a virtual simulation experiment teaching platform for novel distribution system with high penetration of DGs is designed. Based on the experiment teaching platform, four experiments are designed preliminarily: ①Analyze topology of distribution system and verify the connectivity. ②Compare voltage fluctuation of distribution system with different penetration of DGs. ③Calculate power flow of distribution network with different penetration of DGs. ④Calculate distribution network operation status considering the fluctuation of DGs and load. With the establishment of the virtual simulation experiment teaching platform, the electrical engineering students' understanding of power flow calculation of distribution systems is improved, and the teaching effect is comprehensively enhanced.

Integration of distributed energy storage (DES) is beneficial for mitigating voltage fluctuations in highly distributed generator (DG)-penetrated active distribution networks (ADNs). Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. However, absence of network parameters and complex operational states of ADN poses challenges to model-based methods. This paper proposes a data-driven predictive voltage control method for DES. First, considering time-series constraints, a data-driven predictive control model is formulated for DES by using measurement data. Then, a data-driven coordination method is proposed for DES and DGs in each area. Through boundary information interaction, voltage mitigation effects can be improved by inter-area coordination control. Finally, control performance is tested on a modified IEEE 33-node test case. Case studies demonstrate that by fully utilizing multi-source data, the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.