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.

[Objective] The increasing penetration rate of renewable energy has decreased the inertia of power systems. The traditional fixed-parameter frequency control strategy makes it difficult to effectively support system frequency stability requirements. Frequency stability faces severe challenges， especially in renewable energy sending systems. [Methods] To enhance the frequency support performance of VSC-HVDC for sending-end systems with renewable energy， an adaptive dual droop control strategy based on changes in system frequency and DC voltage is proposed. First， a model of renewable energy transmitted by a VSC-HVDC system was established. For frequency droop control， a frequency-adaptive coefficient was designed based on the logistic function of frequency deviation to enable the system to dynamically adjust the droop coefficient and respond flexibly to frequency changes. For the DC voltage droop control， via the coupling relationship between DC voltage and active power using virtual inertia technology， the DC adaptive coefficient was designed as the adaptive virtual inertia coefficient based on the frequency deviation and the rate of change of frequency to enhance the system’s ability to suppress frequency fluctuations. Finally， a simulation model based on the PSCAD/EMTDC platform was established， and a comparison study with the traditional method in different scenarios was performed. [Results] The results show that the proposed strategy reduces the maximum frequency deviation by more than 10% in various scenarios while increasing the maximum utilization of the VSC-HVDC system by over 10%. [Conclusions] Compared with traditional methods， the proposed strategy can significantly reduce the maximum frequency deviation and effectively improve the frequency support performance of VSC-HVDC systems.

With the vigorous development of the digital economy and the rapid construction of novel power system,deeply integrating advanced digital technology with power measurement technology and building a digital power measurement smart laboratory is an important way to accelerate the construction of a modern advanced measurement system and promote the digital transformation of power measurement.Focusing on the shortcomings and challenging problems in the construction of the current power measurement system,this paper describes the overall architecture and technical system of the power measurement smart laboratory in detail.On this basis,it conducts in-depth dis-cussions on key issues such as measurement data panorama perception,edge computing and coordinated regulation,platform interaction and data processing,cross domain integration and value-added services,security protection,etc.during the construction of the laboratory,and looks forward to the realization of the functions and values of the digital power measurement smart laboratory,hoping to provide a certain reference for the development of power measurement technology.

[Objective] To improve the intelligence level of fault recovery in low-voltage substations， a low-voltage passive substation post-fault self-healing strategy based on fog computing load prediction was proposed for the problem of low-voltage substation fault recovery.[Methods] First， to avoid equipment overload caused by network reconstruction， the load level of the network must be determined in advance. Combining the typical structure of low-voltage passive substations and the characteristics of fog computing communication architecture， a fog computing ultra-short-term load prediction method based on the dynamic aggregation of an incremental learning model was designed. This method embedded two ultrashort-term load prediction technologies with complementary characteristics. It used a real-time load for model incremental learning in a dynamic weighted manner and rapidly predicted low-voltage loads in a fault event-triggered manner. In addition， based on the proposed fog computing load prediction， a low-voltage passive substation switch reconstruction self-healing recovery model without line parameters was proposed and modeled as a mixed-integer quadratic programming problem.[Results] The simulation results showed that the average absolute scale-free error of the proposed fog computing load prediction was mainly affected by the load mutation that could be controlled between 5 and 40， and the relative error was between 1% and 8%.[Conclusions] The proposed post-fault self-healing strategy effectively completed the transfer of single-phase loads and maintained the inter-phase load balance as much as possible， while maintaining the radial network operation and avoiding equipment overload.

Current pooled green electricity transactions face dual challenges: inefficient supply-demand matching and the absence of reliable trust frameworks. Addressing these issues calls for a multi-technology approach to establish a flexible and trustworthy trading system. To address this, this paper innovatively integrates the subjective logic trust evaluation model, the blockchain's immutable evidence storage mechanism, and the smart contract's automatic execution framework to construct a multi-technology collaborative trusted contract-based trading model. This model builds a reputation quantification model based on the analysis of transaction behavior characteristics to dynamically depict the evolution law of the credibility of participating entities; design a distributed game matching mechanism based on the firework algorithm to achieve autonomous and optimized matching of participating entities; and further develops a full-process smart contract chain based on blockchain to realize the trusted and automated execution of the closed loop from aggregated declaration, matching to fund settlement. Simulation results show that this solution significantly improves transaction performance compared to the traditional model: the dynamic reputation mechanism enables real-time assessment of the credibility of transaction entities, effectively purifying the trading environment; the consumption rateincreases by 46.5% and the revenue increases by 38.2%; the contract execution delay is reduced by 49.7% and the throughput is increased by 55%.

The current measurement method in the power industry today mainly relies on current sensors. With the development and progress of modern technology, the power system is facing unprecedented challenges. Traditional current sensing technology is difficult to adapt to the pace of the times, and the new generation of current sensing technology is constantly evolving. More and more attention by all walks of life and has been widely used. Tunnel magneto resistance current sensor has the characteristics of high linearity, high sensitivity, low cost, simple structure, etc., so it has become a very potential sensor product. Taking the current sensor developed by tunnel reluctance technology as the core, this paper introduces the working principle of tunnel reluctance current sensor, the advantages and disadvantages of various structures and their application fields, and the improvement methods of different defects. Furthermore, it summarizes the research status and future prospects of sensor array.

Among existing mathematical models of electric energy measurement, either the full electric energy is calculated, or the fundamental active electric energy is conducted, or the fundamental and integer harmonic components of electric energy are separately determined. An electric energy measurement mathematical model that can fully take into account the influence of inter-harmonics is not yet well developed. In this paper, matrix pencil and singular value decomposition algorithm is used. After sampling the grid voltage and current signals, the matrix pencil algorithm is used to extract the characteristic frequency components, amplitudes, and initial phases. Then, the mathematical model of electric energy formed by the voltage and current of any two different frequency components is derived. The relevant electrical parameter information is substituted into the mathematical model, and the fundamental, harmonic, inter-harmonic and other electric energy components are obtained. The proposed algorithm is verified by numerical simulation tests. It is shown that in order to measure electric energy with high accuracy, the influence of inter-harmonics should be fully considered. The proposed new method of electric energy measurement has advantages over the FFT-based electric energy measurement algorithm.

With the proposal of the dual-carbon economy,smart grids are developing in the direction of energy con-servation and emission reduction,and the abnormal power consumption of users has caused serious loss of power re-sources.Aiming at the problems of low accuracy and slow operation efficiency of traditional abnormal power con-sumption detection methods,a lightGBM model combined with an improved long short-term memory network model is proposed for abnormal power consumption detection.Anomaly detection is carried out by combining sampling and lightGBM model,and abnormal electricity consumption category is given by improving long short-term memory net-work model.The advantages of the proposed method are analyzed through experiments.The results show that,com-pared with traditional detection methods,the proposed method can detect abnormal users quickly and effectively,with a detection accuracy of 98.64％,meanwhile,the abnormal data is effectively classified,and the comprehen-sive classification accuracy rate is 96.60％,which provides a certain reference for the development of anomaly de-tection technology.

In order to suppress the safety and stable operation risks brought by the disorderly integration of distributed photovoltaics in the context of the new power system,a hierarchical probability evaluation method based on historical scene statistics is proposed.This method establishes a grading probability evaluation model of PV bearing capacity based on reverse load ratio and a safety check probability evaluation model.The grading probability evaluation process of PV bearing capacity weaknesses based on historical scene statistics is given.By constructing a PV bearing capacity evaluation model based on percentile statistics,a hierarchical probability evaluation method for the capacity of distributed PV access distribution network is finally formed.The effectiveness and universality of the proposed method are verified by the improved guideline example and the actual distribution network case.Experiments show that the method can scientifically show the new PV capacity of each power supply area under different percentiles,and the identified PV capacity weakness is more in line with statistical significance.

A multi-objective optimization control strategy for distribution station SOP is proposed to enhance the distributed generation(DG)consumption capability of the distribution network by utilizing multi soft open point(SOP).By analyzing the access mode of SOP in the distribution network and considering its role in distributed power consumption and peak shaving,a multi-objective model was constructed to maximize the daily consumption of distributed energy,minimize control costs,and minimize the deviation of daily net load of feeder lines.Conduct simulation comparison and verification using an improved distribution network example.The results indicate that SOP integration is beneficial for improving the consumption of new energy and achieving load balancing.

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.

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] To address the insufficient flexibility caused by the large-scale integration of renewable energy into distribution networks and enhance their renewable energy accommodation capacity， this paper proposes a flexibility supply-demand collaborative planning method for distribution networks considering flexibility resource transmission characteristics. [Methods] The proposed method uses distribution network nodes as analytical entities to establish a flexible supply-demand model and a network transmission flexibility resource model. In addition， we developed a two-layer collaborative planning model incorporating the resource transmission characteristics. The upper-layer model aims to maximize the wind/PV integration capacity and minimize comprehensive costs through the coordinated planning of distributed generation， network topology， and energy storage configuration. The lower-layer model optimizes the operational costs through operational optimization. The two-layer model is converted into a single-layer model for the solution via the KKT conditions， ultimately obtaining planning results that satisfy the flexibility requirements of the distribution network. [Results] Simulation results based on a modified IEEE 33-node system demonstrate that， while considering flexibility transmission constraints slightly increases operational costs， the total planning cost decreases by 10%. Under the proposed collaborative planning strategy， as branch transmission margins gradually decrease， the distribution network achieves spatiotemporal matching of flexible supply and demand by significantly increasing the flexible resource dispatch frequency. [Conclusions] The proposed flexibility planning model enhances both renewable energy accommodation capacity and flexible resource regulation capability in distribution networks. A detailed consideration of various costs in the planning and operational stages improves network flexibility while maintaining economic feasibility and meeting distribution network planning requirements. Compared with conventional flexibility planning schemes， this approach effectively avoids the transmission congestion issues encountered in practical operations.

As major national infrastructure projects， UHV projects are key to ensuring power grid security， promoting energy consumption， and optimizing resource allocation. To better meet the requirements of "large-scale centralized construction， high-intensity innovation and research， and high-quality transformation and upgrading" of UHV projects， conducting digital construction of UHV projects based on building information modeling （BIM） technology is imperative. First， this study focuses on the construction of a BIM standard system for UHV projects， designs the framework of the standard system， and discusses the expansion and improvement of the standard system. Subsequently， the construction status of BIM management and control platforms for UHV projects are introduced， and the functions and applications of the subsystems are summarized. Finally， this study analyzes typical application scenarios of BIM technology in the design and construction stages of UHV projects. Based on the current application status of BIM technology in the design， construction， and operation stages of UHV projects， this study summarizes its applicability and practical results and provides experience and suggestions for the digital construction of UHV projects.

To address the problem that existing residual current protection devices are difficult to accurately identify leakage faults in the PV-integrated distribution networks, a leakage fault identification model for photovoltaic-integrated distribution networks based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Newton-Raphson-based optimizer-eXtreme Gradient Boosting (NRBO-XGBoost) is presented. Firstly, the CEEMDAN is used to decompose different leakage signals of the PV-integrated distribution networks. Then, the energy entropy of each decomposed modal component is extracted to construct the leakage fault feature set. Finally, the energy entropy features are input into the NRBO-XGBoost model to achieve the recognition of different leakage states of PV-integrated distribution networks. The effectiveness of the proposed method is verified by the simulation data. The results show that compared with other models, the proposed method has the highest recognition accuracy.

[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.

The construction of new power systems has accelerated the evolution of the form,technology,and functions of the power grid,thus enriching the functions of power grid investment.It is therefore necessary to further understand the functional needs of power grid investment,optimize the investment structure,and further support the transformation of the energy and power structure.This paper uses the system dynamics model to study the changing trends of various forms of the source-grid-load power system during the construction of the new power systems,clarifies the evolution trend of power grid infrastructure investment during the construction of the new power systems,and studies the quantitative evaluation method for investment scale to form a power grid investment demand system that can meet both the needs of traditional economic and social development and the needs of new energy rapid development.

To mitigate the impact of high-penetration photovoltaic (PV) integration on power quality and enhance the reliability of power supply under extreme disasters, mobile energy storage (MES) with its greater flexibility and cost-effectiveness has been identified as one of the effective measures to improve the disaster resistance of distribution networks. This paper proposes a multi-objective optimization model for determining the configuration of MES under high-penetration PV integration scenarios, considering three key aspects including economic efficiency, vulnerability and MES capacity. Additionally, a two-stage resilience-enhancing optimization and scheduling model is developed for extreme disaster scenarios, which incorporates the initial deployment of separable MES and their emergency response dispatch. By dynamically scheduling the access locations and charge-discharge power of the separable MES, the proposed method ensures emergency power restoration under extreme disasters while maximizing the reuse value of MES during both normal and disaster conditions. Finally, case studies validate the effectiveness of the proposed method.

To improve the comprehensive utilization of regional energy and promote low-carbon development,this study constructs an integrated energy system for typical areas,such as parks,including a new energy power generation system driven by photovoltaic and wind power,heating and cooling energy supply systems for ground-source/air-source heat pumps,water chillers,and energy storage equipment.TRNSYS© software is used to simulate and study the dynamic characteristics of the system under six climate conditions in Beijing,and the game theory is used for intelligent operation,which is then compared with the logic control method.The results show that the logic control method can meet the load demand but cannot realize the efficient operation of the heat pump unit and the charge and discharge balance of the energy storage device.The integrated energy system after optimization via game theory can not only realize flexible energy scheduling and distribution through electric-thermal coordination,but also save the entire energy consumption of the heat pump unit and achieve the goal of regional energy economic benefits.The research presented in this paper provides an important theoretical basis for the intelligent operation of heat pump systems in integrated electric-thermal cooperative grids.