The rapid advancement of renewable energy technologies necessitates innovative solutions for the efficient deployment and management of microgrid systems. This paper presents a detailed study on the implementation of edge–cloud collaboration-based plug and play (PnP) and topology identification for microgrids, focusing on the Jingshan AC/DC Microgrid Cluster System (JS-MCS) in Hubei, China. Firstly, the paper elucidates the structure of JS-MCS’s physical system and integral information system, comprising the cloud platform, communication network, edge unit, and terminal unit. The following sections introduce the methods of PnP, describing the overall procedure and specific implementations between cloud and edge and cloud and terminal. Then, a novel approach for topology identification is further explored based on correlation analysis. An exemplary analysis of the studied system, JS-MCS, accentuates practical insights into the application of PnP and topology identification methods.

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.