国网北京市电力公司

[Objectives]To better integrate high-density photovoltaic(PV)energy,energy storage devices are integrated into distribution networks to achieve peak shaving and valley filling of electrical loads and alleviate the effect of distributed PV on grid voltage.[Methods]Through appropriate selection of energy storage scheduling and distributed PV output strategies,a multi-objective optimization method is used to optimize the power flow of distribution networks,aiming to minimize network losses and voltage deviations.A comprehensive evaluation system is established using the analytic hierarchy process-entropy weight method,and an optimization model is developed using particle swarm optimization algorithm to obtain the optimal scheduling strategy for PV and energy storage coordination.[Results]Calculations and analysis are conducted on a 30-node simulation model and practical cases.It is demonstrated that the energy storage system effectively mitigates PV fluctuations,ensures voltage stability at nodes,and improves energy utilization efficiency.Compared with traditional algorithms,the proposed method shows significant advantages in power quality and network loss control.[Conclusions]The method can achieve efficient and stable operation of distribution networks and reduce system network losses.

The overhead line patrol robot plays an important role in improving the line maintenance efficiency and ensuring the safe and stable operation of power system.Aiming at the problem that the overhead line patrol robot needs to effectively identify affiliated obstacles and adopt corresponding obstacle-avoidance actions,this paper stud-ies the deep learning-based affiliated obstacle identification method of overhead transmission line for patrol robots.The overall structure of overhead line patrol robot based on deep learning is discussed.The YOLOv8(you only look once version 8)model and its application in obstacle identification are analyzed based on the overall structure.Fur-thermore,the effectiveness of the proposed method is verified using an augmented obstacle dataset of overhead line.Experimental results show that the proposed overhead line obstacle recognition method has a faster recognition speed and a higher recognition rate,which can meet the obstacle avoidance needs of patrol robots.

ObjectiveTo better understand how the structural characteristics of urban high-voltage cable tunnel networks affect fire propagation, this study aimed to provide a scientific basis for fire prevention, control strategies, and tunnel design optimization. Specifically, fire propagation behavior under different structural configurations and ventilation systems in cable tunnels was investigated. Key fire dynamics parameters analyzed included temperature distribution, smoke propagation, flame spread speed, and toxic gas concentrations for both single-layer and multilayer cable arrangements.MethodsThe investigation relied on three-dimensional fire dynamics simulations using Fire Dynamics Simulator software to explore fire propagation in urban high-voltage cable tunnels. Various tunnel structural configurations were analyzed, including single-layer and multilayer cable arrangements, with and without ventilation systems. Critical fire parameters, such as heat release rate (HRR), ceiling temperatures above the fire source, smoke flow patterns, and toxic gas concentrations, were examined under different fire scenarios. Numerical modeling provided detailed insights into how fire dynamics interact with tunnel structural features, emphasizing the significance of cable layout and ventilation. The study also explored how ventilation affects smoke behavior, assessing its influence on fire spread, temperature, and gas emissions.ResultsThe results revealed distinct differences in fire behavior depending on tunnel structure and ventilation. Multilayer cable configurations caused ceiling temperatures above the fire source to rise significantly faster and reach higher peaks within 400 s compared to single-layer arrangements. This rapid increase in temperature indicated that the denser cable arrangement boosted the HRR, resulting in greater thermal effects. Smoke propagation was highly dynamic. During the early stages, it initially spread rapidly to one side of the fire source. Ventilation systems, however, altered this behavior by reversing the smoke flow direction over time. This reversal created localized temperature increases and more complex smoke distribution patterns. It also introduced cooler air into the system, influencing flame propagation and heat transfer dynamics. Toxic gas analysis showed that carbon monoxide levels were significantly greater for multilayer cables between 300 s and 500 s, indicating more incomplete combustion and increased hazardous gas emissions in these configurations. Flame propagation was faster, and heat transfer effects were more pronounced in multilayer configurations, highlighting the critical role of structural design in fire dynamics. These results underscore the heightened fire hazards posed by multilayer cable arrangements, including faster flame spread, greater thermal effects, and elevated toxic gas concentrations.ConclusionsThis study emphasizes the critical need for optimizing tunnel designs and ventilation systems to mitigate fire risk effectively in urban high-voltage cable tunnels. Multilayer cable arrangements notably increased heat release rates, toxic gas emissions, and flame propagation intensity, exacerbating fire hazards. While ventilation systems can positively influence smoke propagation and control localized temperatures, improper ventilation strategies may introduce risks, such as smoke flow reversal and uneven heat distribution. These findings provide essential technical insights for developing fire safety measures, highlighting the need for prevention and control strategies tailored to the structural and operational characteristics of urban high-voltage cable tunnels. By addressing these challenges, this research helps improve fire resilience, enhance infrastructure safety, and protect personnel during fire emergencies. This study provides a valuable scientific foundation for improving fire safety management in these tunnels, with practical implications for designing safer infrastructure and implementing effective fire prevention strategies.

[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization scheduling method for MEMGs that considers uncertainty correlations and outlier data. First，an ambiguity set incorporating uncertainty correlations is introduced using the copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated，integrating the ambiguity set and opportunity constraints to address the uncertainty correlations. Second，because the distributionally robust optimization model cannot be solved directly，a worst-case transformation method is derived for the proposed ambiguity set using dual theory，McCormick relaxation，and conditional value-at-risk approximation. This transforms the distributionally robust model into a linear deterministic model，thereby enabling an efficient solution using optimization solvers. Finally，a sample-pruning algorithm is proposed，which iteratively generates subsamples from the original dataset by removing the outliers and extreme data points. This approach mitigates the adverse effects of such data on distributionally robust opportunity constraint scheduling results. [Results] Case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set，resulting in an 8.16% reduction in out-of-sample costs. The proposed sample-pruning algorithm further reduces the out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances the scheduling efficiency and ensures reliability，which collectively demonstrates its superiority.

In the context of the continuous growth of distributed photovoltaic (PV) access capacity of the distribution network, the reasonable planning and invocation of flexible resource is the key to promote the efficient absorption of PV. This paper presents an evaluation method of PV hosting capacity of distribution network considering both node type and network type flexible configuration. Firstly, based on the traditional multi-scenario construction method, the concept of time-sequence correlation is introduced establish the joint scenario set of source and load considering time sequence autocorrelation and cross-correlation. Then, the bi-level optimization model of upper PV hosting capacity evaluation and lower flexible resource allocation is constructed. The upper model aims to maximize the PV access capacity, while the lower model considers the flexible operation constraints of the distribution network to promote the efficient absorption of PV. By solving the bi-level model iteratively, the maximum evaluation result of PV hosting capacity and the optimal allocation scheme of flexible resources are obtained. Finally, the simulation analysis shows that the cooperation of node resource and network channel significantly improves the PV hosting capacity and operational flexibility of the distribution network.