To combat energy shortage, the multi-energy system has gained increasing interest in contemporary society. In order to fully utilize adjustable multi-energy resources on the demand side and reduce interactive compensation, this paper presents an integrated demand response (IDR) model in consideration of conventional load-shedding and novel resource-shifting, due to the fact that participants in IDR can use more abundant resources to reduce the consumption of energy. In the proposed IDR, cooling, heating, electricity, gas and so forth are considered, which takes the connection between compensation and load reductions into consideration. Furthermore, a bilevel optimal dispatch strategy is proposed to decrease the difficulty in coordinated control and interaction between lower-level factories and upper-level multi-energy operators in industrial parks. In this strategy, resources in both multi-energy operator and user sides are optimally controlled and scheduled to maximize the benefits under peak shifting constraint. In the normal operation mode, this strategy can maximize the benefits to users and multi-energy operators. Particularly in heavy load conditions, compared to the conventional electricity demand response, there are more types of adjustable resources, more flexibility, and lower interactive compensations in IDR. The results indicate that optimal operation for factories and multi-energy operators can be achieved under peak shifting constraint and the overall peak power value in industrial park is reduced.

Direct current (DC) bus voltage stability is essential for the stable and reliable operation of a DC system. If an oscillation source can be quickly and accurately localized, the oscillation can be adequately eliminated. We propose a method based on the power spectral density for identifying the voltage oscillation source. Specifically, a DC distribution network model combined with the component connection method is developed, and the network is separated into multiple power modules. Compared with a conventional method, the proposed method does not require determining the model parameters of the entire power grid, which is typically challenging. Furthermore, combined with a novel judgment index, the oscillation source can be identified more intuitively and clearly to enhance the applicability to real power grids. The performance of the proposed method has been evaluated using the MATLAB/Simulink software and PLECS RT Box experimental platform. The simulation and experimental results verify that the proposed method can accurately identify oscillation sources in a DC distribution network.

The integration of distributed generation (DG) into distribution networks has significantly increased the strong coupling between power supply capacity and renewable energy acceptance capacity. Addressing this strong coupling while enhancing both capacities presents a critical challenge in modern distribution network development. This study introduces an innovative joint planning and reconstruction strategy for network and energy storage, designed to simultaneously enhance power supply capacity and renewable energy acceptance capacity. The proposed approach employs a bi-level optimization model: the upper level focuses on minimizing economic costs by determining the optimal locations and capacities of energy storage systems and the layout of network lines, while the lower level aims to maximize power supply and renewable energy acceptance capacities by optimizing line switch states. Additionally, this research quantifies the coupling relationship between these two capacities under uncertainty, providing a deeper understanding of their dynamic interaction. Advanced computational techniques, including Monte Carlo simulations and particle swarm optimization (PSO), are utilized to solve the model efficiently. Case studies demonstrate that the proposed strategy effectively enhances both power supply and renewable energy acceptance capacities. Furthermore, exploring the strong coupling relationship between these two capacities under various conditions not only optimizes the utilization of renewable energy in the power system and prevents resource waste, but also helps avoid the volatility impacts of renewable energy uncertainty on the power system in actual planning. Additionally, the network and energy storage joint planning and reconstruction strategy proposed in this study achieves cost minimization under the constraint of limited resources and simultaneously enhanced both capacities. The strategy provides feasible solutions for power grid planning in actual applications.

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation.

DC distribution systems are a typical power electronic system with low inertia, low-rotational kinetic energy, and poor antidisturbance capability when loads fluctuate or parameters change. In this paper, a virtual inertia control with an additional first-order filtering link is proposed on the basis of P-U_dc droop control. The results of the simulations and experiments verify that the additional inertia control reduces the voltage change rate and improves the system inertia by adjusting the virtual capacitance value on the DC side of the converter, which can achieve a smoother and more accurate voltage control and suppress the continuous voltage oscillation.

With the increasing number of electric vehicles (EVs), the randomness of the charging load will have an increasing impact on the distribution network (DN) and road network. Different guidance strategies lead to different network-related capabilities of fast charging stations (FCSs). In this paper, a hierarchical and comprehensive evaluation method is proposed for the network-related capability of FCSs. Based on the comprehensive evaluation method, a charging guidance strategy is proposed to improve the network-related capability of FCSs. Finally, the network connection capability of FCSs under four strategies is comprehensively evaluated to verify the effectiveness of the proposed method.