To address voltage over-limit and instability issues caused by high penetration of distributed photovoltaic in distribution grids, this paper proposes a voltage regulation strategy based on distributed photovoltaic cluster partition. Firstly, a dual-criterion modularity function considering net load and equivalent electrical distance is constructed. A community detection algorithm based on modularity (i.e. fast-unfolding) is applied to dynamically partition photovoltaic clusters. Secondly, differentiated voltage regulation is designed according to the severity of cluster over-limit conditions: intra-cluster reactive power adjustment for mild over-limits, and multi-device hierarchical collaborative control for severe over-limits, with task allocation based on response speed and economic priority. Finally, an optimization model targeting minimization of network losses and voltage deviation is established. The improved multi-organization particle swarm optimization (MPSO) algorithm, combined with niche-based techniques, is employed to determine the optimal regulation sequence and device action levels. Simulation results demonstrate that this method effectively controls voltage fluctuations, reduces network losses, and enhances system stability in both the modified IEEE 33-node system and the IEEE 123-node system.

Traditional evaluation methods predominantly rely on the center of inertia frequency index, which only reflects the overall average frequency dynamics of the network, ignoring the significant characteristics of time-varying inertia and uneven spatial distribution under high penetration of renewable energy. To address the issue of varying system inertia levels caused by large-scale wind power participating in inertia support and frequency regulation, this paper proposes an inertia demand evaluation method for wind power-integrated power systems considering the spatiotemporal characteristics of inertia. Firstly, a system frequency response model incorporating wind power comprehensive control is constructed by integrating wind power virtual inertia response and pitch angle primary frequency regulation control. The system transfer function and the calculation formula for the equivalent inertia time constant are derived, clarifying the support mechanism of wind power in delaying the rate of change of frequency through rapid response to active power disturbances. Secondly, a characterization method considering the spatiotemporal characteristics of inertia is proposed. Based on the analysis of the nodal power-frequency mechanism, a node inertia matrix and a temporal dynamic model are constructed to quantify the temporal evolution laws of inertia at the same node and the spatial distribution differences among different nodes, thereby overcoming the limitations of traditional center of inertia frequency evaluation. Furthermore, by obtaining the system power-frequency equation and frequency response model, an inertia time constant model is constructed to form an inertia demand evaluation model considering spatiotemporal characteristics. Finally, simulation verification is conducted based on the modified IEEE 10-machine 39-bus system. The results demonstrate that the participation of wind power can effectively enhance the system's inertia support capability. The proposed method accurately captures inertia differences across different nodes at different times and realizes the visualization of spatiotemporal characteristics through inertia heat maps, providing a quantitative basis for inertia configuration in the planning stage and frequency stability control in the operation stage of new power systems.

With the gradual complexity of the active power-frequency coupling characteristics of the new power system, the traditional single energy storage grid-connected frequency regulation strategy has brought huge pressure to the primary frequency regulation of the power system. The participation mode of multi-type energy storage in primary frequency regulation and the frequency characteristics of the power grid under the coordinated control of energy storage need to be studied urgently. This paper studies the frequency response mechanism of electrochemical energy storage based on droop control and flywheel energy storage based on virtual synchronous machine control respectively. When multi-type energy storage participates in the primary frequency regulation of the power system, the low-pass filter link is used to process the frequency change rate signal to achieve the coordinated control effect of energy storage. Then, the coordinated control model of multi-type energy storage is combined with the power system containing new energy and traditional thermal power, and the frequency response model of the power system is established. The model is used to quantitatively analyze the influence of energy storage-related parameters on the system frequency change rate and steady-state frequency deviation, and the parameter sensitivity analysis is carried out. Finally, the model is built on Matlab/ Simulink to verify the influence of multi-type energy storage-related frequency regulation parameters on the system frequency characteristics. The research proves that considering the coordinated control of multi-type energy storage in the frequency regulation unit of the new power system can improve the frequency stability characteristics of the power system.

Shared energy storage can effectively address the issues of low utilization and high costs caused by individual energy storage configurations by regulating resources across multiple regions. To further exploit the potential of shared energy storage in demand-side resources, this paper introduces electric vehicles and ice storage air conditioning, both with flexible energy storage characteristics, to construct a generalized shared energy storage model for the coordinated optimization of energy usage in smart building clusters. In response to the uncertainty of photovoltaic (PV) output on the energy input side, a time generative adversarial networks (TimeGAN) is employed to simulate a large number of PV output scenarios. By combining daily irradiance data, the static and dynamic features of these scenarios are mined, and typical scenarios are identified using K-medoids clustering. Additionally, a tiered carbon trading mechanism is introduced to limit the carbon emissions of the energy system. An optimization scheduling model for smart buildings is established, considering operational costs, carbon emissions, and user comfort, and is solved using CPLEX. Case studies demonstrate that the proposed method can generate high-quality PV output scenarios, improve regional PV consumption rates, and effectively balance user comfort and costs.

When discharging, electric vehicles can serve as distributed energy storage units of the power grid to alleviate the power supply pressure of microgrids with high proportions of new energy integration. Capitalizing on the characteristics of time-of-use tariffs across multi-time scales, this study proposes a multi-time scale optimization scheduling method for microgrids that takes into account clusters of electric vehicles. In day-ahead scheduling phase, the equipment output such as internal energy storage, interruptible loads and transferable loads in the microgrid is optimized based on time of use tariffs; During intra-day optimization scheduling phase, electric vehicle clusters will be included in the energy scheduling of microgrids, and reasonable charging and discharging can be achieved by analyzing the scheduling potential of each electric vehicle cluster. To verify the effectiveness of the proposed scheme, electric vehicle clusters are selected to participate in microgrid energy scheduling based on variable time of use tariffs during peak, flat, and valley periods. The results show that the multi-time scale optimization scheduling for microgrids considering the participation of electric vehicle clusters can make full use of the energy storage resources of electric vehicle clusters and improve the flexibility and economy of microgrid scheduling operation.

Dynamic equivalent modeling of large-scale wind farms is the foundation for studying wind power grid integration, while the clustering-based equivalent model of wind farms cannot fit the dynamic output characteristics with high accuracy, and the poor generalization ability in its application is an inherent defect of clustering based model. Aiming at this problem, this paper proposes a wind farm equivalent modeling method based on particle swarm optimization-long short term memory neural network-error correction model (PSO-LSTM-ECM). Firstly, K-means clustering algorithm and capacity weighting method are used to cluster wind turbines in wind farms, and a clustering equivalent model of the wind farms is constructed; Then, ECM is constructed based on the transient response errors of the detailed model and the clustering equivalent model, and the correction model is obtained through the LSTM neural network training optimized by PSO, and the output value of the network is compensated to the clustering equivalent model; Finally, a joint simulation is conducted on PSCAD and Matlab platforms to compare and analyze the detailed wind farm model, clustering equivalent model, and the model proposed in this paper. The result proves the effectiveness and superiority of the proposed model.