Urban Power Grid Research Articles

ChatEV: Predicting electric vehicle charging demand as natural language processing

The increasing popularity of electric vehicles (EVs) in recent times has introduced considerable load conditions for urban power grids and transportation systems, which highlights the importance of accurately predicting charging demand to enhance charging efficiency. However, current forecasting methods still face challenges in effectively aligning diverse data and generating accurate predictions that can be applied to unseen scenarios. To overcome the challenges, this work introduces a novel perspective: employing large language models (LLMs) as EV charging demand predictors. First, we reformulate the prediction task into a text-to-text format, enabling seamless and effective alignment of various features within a unified language semantic space. Subsequently, we fine-tune a LLM using a meta-learning framework to adapt it specifically for EV charging prediction. Through comprehensive evaluations, it has been demonstrated that the proposed model, ChatEV, achieves outstanding performance in EV charging demand forecasting, particularly in scenarios with limited data.

Regulation techniques and applications of distributed load resources in urban power grids based on internet of things

The power consumption of urban power systems is increasing rapidly with two typical trends: the first one is that the daily peak-valley difference of loads is more significant, and the power supply is tight during peak hours, which threatens the system’s safe and stable operation; the second one is that the load energy efficiency in urban power systems is not high, which is the primary source of carbon emission in the power industry. Therefore, reducing the peak power and improving the system’s energy efficiency are urgent tasks for enhancing the system’s security and achieving the carbon emission goals. The rapid development of the Internet of Things (IoTs) ushers new opportunities for regulating demand-side loads. By analyzing the technical characteristics of load control based on IoTs, this paper investigates the modeling methods of load resources. On this basis, different control and optimization methods of load resources are analyzed and compared thoroughly. Besides, considering that load control is not only related to technical methods but also impacted by incentive strategies, the load control mechanisms under the mature and immature market environments are analyzed. Finally, the research gap and prospect of load regulation are proposed.

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

Scenario construction and vulnerability assessment of natural hazards-triggered power grid accidents

In light of escalating urbanization trends and climate change impacts worldwide, the susceptibility of urban power grids to natural disasters has become an overarching global concern. Prior research has predominantly concentrated on singular calamities while often disregarding cumulative repercussions from multiple concurrent events affecting power grid resilience. This investigation presents an exhaustive framework for assessing grid vulnerabilities by quantifying diverse impacts from potential natural disaster scenarios and delineating adaptive pathways for evaluating inadvertent occurrences. The framework amalgamates an extensive array of metrics— including probability assessments, system state evaluations, trigger threshold analyses, responsiveness measurements, and adaptability adjustments— within a dynamic scenario-oriented model. The inquiry progresses through distinct stages: formulating an all-encompassing methodology for assessing vulnerabilities; assessing varied impacts stemming from different environmental perils; mapping out post-disaster evolutions; and executing a case analysis focusing on an urban power grid.Concentrating specifically on rainfall, snowfall, and freezing incidents, the case analysis uses locale-specific data to appraise grid susceptibilities while employing multi-criteria decision analysis (MCDA) to facilitate decision-making. During this deliberative process, optimal strategies are derived, and mitigative actions are recommended with the aim of diminishing power-grid vulnerabilities. This investigation underscores intricate risk dynamics within urban power grids while presenting a feasible framework for sustainable planning and effective emergency responses in confronting natural hazards.

A hierarchical urban power grid disaster response method based on automatic voice warning technology

This article proposes a hierarchical and graded urban power grid disaster response method based on automatic voice warning technology. This method combines automatic speech technology and hierarchical classification methods to achieve fast and accurate information transmission and response. This article first introduces the background and significance of this method, and then introduces the current research status in China. Next, this article elaborates in detail on the experimental dataset and preprocessing, CNN and RNN algorithm model design, experimental results and analysis, and the implementation of speech warning using TTS speech synthesis technology. Finally, this article summarizes the main work and contributions of this article, and points out future research directions.

A Two-Layer Framework for Mitigating the Congestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

A Two-Layer Framework for Mitigating the Congestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

Mitigation Strategy of Neutral-Point DC for Transformer Caused by Metro Stray Currents

Metro stray currents flowing into neutral-point-grounded transformers can cause serious direct current (DC) bias. Affected by both metro train and urban power grid operations, transformer neutral-point DC caused by metro stray current is complex, dynamic, and time-varying, which changes the DC bias risk level of transformers. Thus, just installing blocking devices (BDs) at transformer neutral points with high instantaneous DC may make it difficult to comprehensively mitigate DC bias and lead to increased BD installation. To solve this, through optimizing BD installation placements, a mitigation method for transformer DC bias is proposed. In the proposed method, the DC bias risk level and BD installation number are included in the constraint and objective functions. To evaluate the risk level of transformer DC bias, four indicators are proposed considering the distribution characteristics of neutral DC. To optimize the BD installation placements, the effects of both the metro train dynamic operation and the operation topology of the urban power grid on neutral DCs are considered. The Monte Carlo method is used to sample the train operation conditions and a relation matrix among transformers is established. Applying the method to a certain power system, the result of BD installation placements shows that the transformers supplying the metro system must have BDs installed at their neutral points.

Low-carbon development of urban power grids in China: Quality assessment, obstacle analysis, and potential release

Urban power grid planning plays a pivotal role in the process of reducing carbon dioxide emissions from power grids. Traditional grey target models and analytic hierarchy processes fail to reflect the diversity of resource endowments in urban areas and cannot provide targeted solutions for carbon reduction strategies in urban power grids, representing a critical issue that urgently needs addressing. This paper introduces an enhanced grey target model based on the ‘source-grid-load’ system, establishes a quality evaluation index system for low-carbon development, and employs the Analytic Hierarchy Process - entropy weight method to account for the differentiated impacts of urban power grid partition resources. It conducts a comprehensive assessment of overall potential through multi-dimensional relative target centrality and identifies and examines key factors in low-carbon development potential using single-dimensional relative target centrality. The findings indicate that the refined grey target model, focusing on relative target centrality, effectively identifies areas with significant low-carbon development opportunities and assesses their developmental scope across various metrics, thereby providing strategic insights for carbon reduction planning in urban power grids under multi-objective decision-making conditions. This method will decrease CO2 emission by 21.38 % in 2030.

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

Performance Optimization Design Study of Box-Type Substations Subjected to the Combined Effects of Wind, Snow, and Seismic Loads

As a pivotal node in both urban and rural power grids, the box-type substation not only serves the functions of power conversion and distribution but also need to provide structural support and environmental adaptability. However, deficiencies in strength, stiffness, or vibration characteristics may lead to vibration and noise issues, and extreme environmental changes can pose risks of structural damage. This study aims to verify and optimize the seismic resistance and environmental adaptability of box-type substations through finite element simulation methods. Using SOLIDWORKS, a three-dimensional model of the box-type substation was constructed, and static and dynamic analyses were conducted using Ansys Workbench to comprehensively evaluate the dynamic response of the box-type substation under wind, snow loads, and seismic action. Through iterative simulations and a comparison of multiple design solutions, the structural optimization of the substation was achieved. The optimized structure balances strength and stiffness, significantly reducing the weight of the substation body, with the wall thickness reduced by 60%. Additionally, the phenomenon of stress concentration on the side walls was eliminated, ensuring that the equivalent stress is below the material yield strength. This research provides methods and empirical results for enhancing the performance and reliability of box-type substations under seismic conditions, confirming the feasibility of a lightweight design, while ensuring structural safety.

ANALYSIS OF REACTIVE POWER COMPENSATION MEANS IN ELECTRIC NETWORKS OF AZERBAİJAN

The article analyzes the existing reactive power compensation in the electrical systems of Azerbaijan, and also examines the problems and shortcomings that arise in the reliable supply of electrical energy to consumers and provides information about the sources of RP that are produced in the industry of the republic. The most important task that requires solving the problem of improving the quality of electrical energy in the country's networks, both at the design stage and at the operation stage of electrical power supply systems, is reactive power compensation. Based on this, it is necessary to select RP sources, their favorable placement, regulation and calculation of their power in accordance with the existing power supply system. Benefits RP in industrial electrical networks include many issues, the solution of which is aimed at increasing the effective power factor and stability of electrical devices, their efficiency, improving the quality of electricity and, in general, the reliability of the electrical system. Currently, there are still issues that have not been resolved and require a special approach for their implementation. Such important issues include: determining the installation of compensation devices, choosing their number, type, safe operation and protection. In the industrial power grids of Azerbaijan, issues of automatic control of RP are at the development stage and for this development, adequate mathematical models of a rational RPK system with minimal losses are being considered. To solve the above, targeted scientific methods are created. The problem of RPK in modern electrical systems of Azerbaijan is important for the following reasons: 1) the consumption of reactive energy increases due to an increase in loads with the consumption of reactive current in the country’s electrical networks; 2) the growth of RP consumption is accelerating compared to active consumption in many sectors of industrial production; 3) there is an increase in reactive energy consumption not only in large urban power grids, but also in small rural ones. Keywords: reactive power, compensating devices, electrical devices, energy, system, quality, synchronous compensator, static capacitor.

A parameter-free fault location method for cross-bonding cable based on ranging equations

With the increasing application of long-distance high-voltage cables using the cross-bonding grounding method in urban power grids, the effectiveness and accuracy of the single-phase short-circuit fault location method for cross-bonded cables have gradually gained attention. To reduce positioning errors caused by inaccurate or unknown line parameters, a fault location method independent of line parameters is proposed. Firstly, the changes in sheath circulating current before and after the fault occurrence are analyzed, and based on these changes, a criterion for fault identification is established to determine the approximate range of fault occurrence. Secondly, considering the three-phase electromagnetic coupling of cross-bonded cables, an equivalent model for single-phase short-circuit faults in cross-bonded cables is established based on the double-π model. The along-line voltage and current are calculated by deducing electrical quantities at the ends of the cable. Then, the fault location equations are formulated based on the fault section, and the trust-region algorithm is employed to solve for fault distances and unit-length parameters of the cable in each equation group. Finally, a cable fault model is built using the PSCAD/EMTDC simulation platform, and the influence of different factors on the fault location method is analyzed. The results show that the fault location error of the proposed method is around 0.2 %, demonstrating high accuracy.

First-principles insight into a Nb-embedded PdSe2 monolayer for dissolved gas sensing in oil-immersed transformers of a regional power grid

As the integration of renewable energy into the regional urban power grid, guaranteeing the safety of oil-immersed transformers emerges as a top priority. Dissolved gas analysis by nanomaterial-based gas sensor is a workable technique to evaluate the operation status of the oil-immersed transformers. This work purposes Nb-embedded PdSe2 (Nb-PdSe2) monolayer as a potential gas sensor upon C2H2 and C2H4, two typical dissolved gases, using the first-principles theory. One Se atom of the pristine PdSe2 monolayer is substituted by one Nb atom to establish the Nb-PdSe2 configuration, which is spontaneous at room temperature with binding energy of −1.81 eV. The adsorption characteristic of Nb-PdSe2 monolayer upon two gas species is analyzed and compared with other metal-doped TMDs. The analysis of electronic property reveals the favorable change in the bandgap of Nb-PdSe2 monolayer as well as the Nb-C bonding nature, leading to the strong sensing response of −99.99% and −99.87% in C2H2 and C2H4 systems using the frontier molecular orbital theory. These findings uncover the strong potential of Nb-PdSe2 monolayer as a resistive gas sensor upon two gases, which would be helpful to explore novel PdSe2-based gas sensor for application in some other fields.

Multivariate Evaluation of Photovoltaic Utilization Potential of Primary and Secondary School Buildings: A Case Study in Hainan Province, China

Modernization and industrialization have significantly increased energy consumption, causing environmental problems. Given that China is the largest energy user, the rise in building energy consumption necessitates clean energy alternatives. The purpose of this study is to summarize typical building models for primary and secondary schools in Hainan Province, and to use software to simulate and calculate the photovoltaic utilization potential of primary and secondary school buildings. In China, the government is usually the manager of primary and secondary schools, and due to their architectural characteristics, these buildings can be used to assess photovoltaic applications. The aim is to drive the application of photovoltaic systems in all types of buildings and promote urban energy reform. This study summarizes the types of primary and secondary school buildings in Hainan Province and analyzes them. It evaluates rooftop photovoltaic projects at the Second Middle School and the Siyuan School in Wanning City, Hainan Province, and uses PVsyst 7.2 software to assess the photovoltaic utilization potential. The results show that the optimal orientation in Hainan Province is south-facing, and the optimal inclination angle is 10° to 20°. The most favorable orientations of facade photovoltaic systems are 20° southeast or southwest. The longest dynamic investment payback period is approximately 15 years, and the environmental benefits are $0.012/kWh. The findings indicate significant potential for photovoltaic applications in primary and secondary school buildings. A combination of facade and rooftop photovoltaics can result in the zero-energy consumption of these buildings, reducing the pressure on urban power grids and achieving sustainable utilization.

Research on vibration response of 550 kV GIL isolation compensation unit against earthquake loading

To analyze the vibration response of Gas Insulated Transmission Lines (GIL) of urban power grid under different seismic intensity levels, based on the actual structure of the standard isolation compensation unit (isolation unit and compensation unit) of 550kV GIL, A fine 3D model of isolation compensation unit segment of 550 kV GIL was established, and the actual vibration response of each standard unit segment of GIL under different seismic conditions was studied by response spectrum analysis. This study clarified the action mechanism and law of GIL vibration characteristics under seismic conditions of urban power grids, provided the relative support for the research of shock absorption of GIL, and was great significance to the actual project of GIL.

Research on relay protection technology of high temperature superconducting cable system

High-temperature superconducting cables have many advantages such as low line loss, large transmission capacity, small space occupation, and environmental friendliness, which provide a highly efficient, compact, reliable, and green way of power transmission for power grids, and have great potential for application in large-capacity power transmission and urban power grid expansion and reconstruction. However, high-temperature superconducting cables have a complex structure, which is quite different from traditional cable lines in terms of operating conditions and fault mechanisms, and the requirements for the secondary system are also more stringent. Relying on the project of China Power Grid, “Research and Demonstration Application of Superconducting Transmission Technology in Urban Distribution Networks”, the thesis carries out an in-depth research on relay protection technology adapted to the access of high-temperature superconducting cables.

Carbon emission reduction contribution analysis of electricity enterprises in urban green development: A quantum spherical fuzzy sets-based decision framework

As managers of urban power grids (UPGs), electricity enterprises are responsible for ensuring energy security and stability during the urban green transformation process. They are also a key driving force in promoting green power transmission and integration. Therefore, exploring the green attributes of UPGs and accurately enhancing their green support capabilities are of significant importance. This study addresses the perceived contributions of electricity enterprises to carbon emission reduction and proposes a decision framework within a quantum-spherical fuzzy (QSF) environment. First, from the perspectives of economy, technology, policy, and management, a carbon emission reduction contribution evaluation system for electric enterprises was constructed. In the QSF environment, this study identifies the importance of various attributes in the evaluation system. Subsequently, an electric enterprise carbon emission reduction perception model was developed based on the preference ranking organization method for enrichment evaluations and prospect theory (PROMETHEE-PT). An empirical validation of the proposed model was conducted in Hefei, China, demonstrating the robustness and effectiveness of the evaluation framework. The study's results were verified by comparing them with those of other methods.

Implementation Strategy of Guangxi Urban Resilient Grid Based on Integrated Utilization of Distributed Renewable Energy and Buildings in the Internet Era

Abstract Facing global climate change and energy crises, urban power grids, particularly in Guangxi, need resilience enhancements to cope with these unprecedented challenges. This study proposes a resilience improvement strategy by integrating distributed renewable energy with building systems, tailored to Guangxi’s unique conditions. We demonstrate that optimizing the network structure and protection settings significantly enhances fault recovery capabilities through system analysis, model development, and simulation. Implementing a distributed multi-microgrid system increased the resilience power supply rate by an average of 15% across eight cities in Guangxi, with a 20% improvement in the system’s resilience emergence rate. This was achieved by optimizing power interactions using a dynamic consistency algorithm, showcasing a viable pathway to bolster urban grid resilience through renewable integration and offering insights applicable to similar urban settings.

Evaluation and Analysis of Urban Power Grid Operation Status Based on Online Sequence Extreme Learning Machine and Self-Coding Network

Evaluation and Analysis of Urban Power Grid Operation Status Based on Online Sequence Extreme Learning Machine and Self-Coding Network

Multi-harmonic sources identification and evaluation method based on cloud-edge-end collaboration

With the increase of power electronic devices such as distributed renewable energies, energy storage, and flexible loads, the disturbance sources are rapidly raised and distributed in multiple voltage levels in the distributed network. The harmonics they produce are random and uncertain, and the massive amounts of data propose high demands on server computing capability, which restrict harmonic sources evaluation and significantly influence the judgments of the power quality in the distribution network. In order to improve the power quality and the harmonic source evaluation function of the distribution network, in this paper, a framework for multi-harmonic source identification and evaluation based on cloud-edge-end collaboration is proposed. Firstly, based on the analysis of the functional requirements harmonic sources evaluation in multiple voltage levels, a novel framework of cloud-edge-end collaboration is proposed. Secondly, the cloud-edge-end collaborative harmonic sources evaluation method which executes different service strategies according to voltage levels and grid operation based on the multi-level interaction of edge computing is proposed. Further, the working principle of the proposed novel framework with multiple evaluation methods including the dominant harmonic source identification, concentrated multiple harmonic sources evaluation (CMHSE) and hierarchical harmonic sources evaluation (HHSE) is described in detail in order to show the completed work processes of the proposed framework. Finally, the performance of the proposed method is verified based on the real data measured from different realistic urban power grids in China. The proposed method can effectively evaluate harmonic contributions in distribution systems and in turn provide a basis for harmonic mitigation measures.