Direct-current (DC) microgrids have gained worldwide attention in recent decades due to their high system efficiency and simple control. In a self-sufficient energy system, voltage control is an important key to dealing with upcoming challenges of renewable energy integration into DC microgrids, and thus energy storage systems (ESSs) are often employed to suppress the power fluctuation and ensure the voltage stability. In this paper, the performances of three voltage control strategies for DC microgrids are compared, including the proportion integration (PI) control, the fuzzy PI control and particle swarm optimization (PSO) PI control. Particularly, two kinds of ESSs including battery and advanced adiabatic compressed air energy storage (AA-CAES) with different operational characteristics are installed in the microgrid, and their impacts on voltage control are investigated. The control performances are comprehensively compared under different control schemes, various scenarios of renewable energy fluctuations, participation in the control of the two ESSs or not, and different fault conditions. Additionally, the dynamic performances of the ESSs are exhibited. The results verify the validity of the control schemes and the feasibility of the configuration of the ESSs into the DC microgrid.

With the rapid development of renewable energy, microgrids are becoming more and more essential in distribution networks. However, uncertainties brought by new energy sources have posed great challenges to the energy safety and stability of distribution networks, especially in the process of fault restoration. To address these issues, this paper investigates an emergency power supply recovery strategy for distribution networks by considering the support capability of microgrids and the high-dimensional dynamic correlations of uncertainty. Firstly, the structure of distribution networks including microgrids is analyzed. Then, the high-dimensional dynamic vine copula model is proposed to model the joint output uncertainty process. Next, island partition and operation models are established, including the objective function and constraint conditions, and the model solving methods are also presented. Finally, an example analysis is conducted to verify the effectiveness of the proposed strategy. The results show that the proposed strategy is able to deal with the uncertainties brought by renewable energy sources in distribution networks with microgrid support, improving energy safety and stability. This research provides valuable insights for the development of emergency power supply strategies in distribution networks.

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). First, the topology of the LVDG is represented as a graph structure using measurement data collected from intelligent terminals, with a feature matrix encoding the basic information of each entity. Secondly, the meta-path form of the heterogeneous graph is designed according to the connection characteristics of the LVDG, and the walking sequence is enhanced using a heterogeneous skip-gram model to obtain an embedded representation of the structural characteristics of each node. Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. Finally, case studies on five urban LVDGs in the Wuhan region are conducted to validate the effectiveness and practicality of the proposed F-GAT model.

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.

To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into the distribution planning process, but there is no quantitative analysis of the flexible resources inside the virtual power plant. At the same time, the traditional planning process does not pay much attention to the acquisition of photovoltaic and load data. Therefore, in this paper, we propose a distribution network planning method considering the flexibility of distributed energy resources in virtual power plants. Firstly, taking the distribution network planning including the virtual power plant as the research object, the flexibility of the distributed energy resource of the virtual power plant was quantified. Then, in order to achieve the goal of minimizing the operating cost of system planning, a distribution network planning model considering the flexibility of distributed energy resources in the virtual power plant is established. In this model, the impact of virtual power plants flexibility on the distribution network planning process is mainly considered. Secondly, this paper uses the improved k-means clustering algorithm to obtain the typical data of PV and load. The algorithm effectively overcomes the impact of PV and load output fluctuations on the planning process. Finally, the simulation results show that the proposed planning model can effectively reduce the operation cost of system planning by using distributed energy storage system and distributed energy resource flexibility. At the same time, the PV absorption rate of the PV power station inside the distribution network is improved.

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage fluctuations, a modified piecewise aggregate approximation (MPAA) algorithm is developed to preprocess raw measurement data through compression and denoising while preserving key voltage correlation features. Second, electrical similarity among customers is explored using the Modified Piecewise Aggregate Approximation K-means (MPAA-K-means) algorithm, enabling preliminary identification of transformer–customer relationships. Then, a training paradigm based on PGAT is introduced to characterize node features constrained by grid topology and electrical properties, achieving refined identification of transformer–customer relationships. Finally, testing results on real LVDG demonstrate the effectiveness and accuracy of the proposed two-stage identification strategy, providing new insights for transformer–customer relationship identification.