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.

The internal battery parameters of the lithium-ion battery energy storage system may be inconsistent due to different aging degrees during the operation, and the thermal effect can also threaten the safety of the system. In this paper, based on the second-order resistor–capacitor equivalent circuit model and the dual extended Kalman filter (DEKF) algorithm, an electrical simulation model of a LIB pack with inconsistent parameters considering the thermal effect is established, in which state of charge (SOC) and state of health (SOH) are estimated using DEKF, while the temperature is calculated by a thermal module. The simulation results show that the DEKF algorithm has a good effect on battery state and parameter estimation, with the root-mean-square error of voltage is lower than 0.01 V and SOC mean absolute error (MAE) is below 1.50%, while SOH error is 3.37%. In addition, the thermal module can provide an accurate estimation of the inconsistent temperature rise of the battery pack, and the MAE between the model-calculated temperature and the experiment is no more than 6.60%. The results provide the basic data for the scale-up of the electrothermal co-simulation model of the LIB energy storage system.

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.

Fluctuations in the output power of a high penetration distributed generation (DG) will result in severe voltage fluctuations/violations in the power distribution station area (DSA). This paper proposes the use of Soft Open Point (SOP) for DSA interconnection to alleviate voltage violations and frequent fluctuations. Firstly, the typical topology of a flexible interconnected DSA is introduced, and then the correlation between active/reactive power fluctuations and voltage fluctuations in DSA is analyzed, according to which a local control strategy is proposed to achieve voltage control by relying only on the local electrical information of the nodes connected to the SOP. Finally, the effectiveness of the proposed control strategy is verified by simulation case. The simulation results show that the voltage fluctuation caused by the DG output power fluctuation can be suppressed within tens of milliseconds through the control strategy. This strategy greatly reduces the communication burden and has good real-time performance. It can be used as an SOP control strategy under conditions of communication failure or to realize a plug-and-play SOP, which has significant engineering importance for solving the voltage violations and frequent fluctuations in DSA and improving the consumption capacity of DGs.

Grid-tied distributed generators (DGs) need to be equipped with anti-islanding protection to avoid the impact of unplanned islanding, which would affect system stability, auto-reclosing, and personal safety. Among the active anti-islanding protections, impedance measurements based on signal injection have the advantages of a low non-detection zone (NDZ) and are less prone to maloperation during grid disturbances; however, there are problems with signal interference in multi-DG systems. Hence, the impedance angle measurement method with signals injected synchronously is proposed. In this method, each DG injects phase-coherent signals to detect islanding using zero-cross points of the voltage (ZCPV) to avoid the protection failures caused by signal interference. An islanding identification criterion based on the measured impedance angle is proposed by analyzing the impedance characteristics of grid connection and islanding, which avoids the influence of variation of the DG operation state on islanding detection. Finally, we present a signal injection strategy and performance analysis in combination with an existing DG control platform, avoiding additional hardware investment. RTDS-based simulation verification shows that the proposed method can 100% avoid DG maloperation due to voltage and frequency disturbances during grid-connected operation and exit operation within 2 s when islanding occurs.