Fast deduction method for new energy distribution networks integrating conventional and extreme scenarios based on graph computing

Abstract

Aiming at the problems of dynamic topology and strong output uncertainty in distribution networks caused by highpenetration new energy integration, as well as the core pain points of traditional situation deduction methods including low computational efficiency, incomplete scenario coverage, and insufficient deduction accuracy under extreme scenarios, this paper proposes a fast operation situation deduction method for high-penetration new energy distribution networks integrating conventional and extreme scenarios based on graph computing.Firstly, a physical-information fusion attribute graph model of the distribution network is constructed to realize the unified representation of topology, electrical characteristics and uncertainty information, as well as multi-scale dynamic topology adaptation. Secondly, a conventional-extreme scenario fusion modeling framework is established. Through scenario normalization, K-means clustering reduction and graph attention network (GAT) correlation learning, key representative scenarios covering the core characteristics of the two types of scenarios are screened out.Then, a storage-computation integrated graph computing core engine is designed based on the power supply service panorama graph. Combined with spatio-temporal parallel deduction and incremental reuse mechanism of time-evolution graphs, the deduction efficiency is greatly improved.Meanwhile, a GCN-LSTM fusion model is introduced to realize multi-step situation prediction and accurate risk identification of distribution networks.Experiments are carried out on a county-level distribution network with 1200 nodes and 52% new energy penetration. Compared with the traditional Newton-Raphson method and GCN-based deduction method, the results show that the single-scenario power flow deduction time of the proposed method is only 800 ms, and the parallel deduction time of 25 key scenarios is 2.3 s. The efficiency is improved by more than 50% compared with the traditional method, and the state estimation speed is increased by 83.3% due to the incremental reuse mechanism.The voltage/power flow prediction error of the proposed method is lower than that of the comparison methods, with a scenario coverage of 95% and a risk identification accuracy of over 96%. The method can be seamlessly integrated with the distribution network automation system, and effectively supports the conventional operation monitoring and extreme scenario emergency disposal of high-penetration new energy distribution networks, with significant theoretical value and engineering application prospects.