With economic development and the increasing electricity demand, distribution network operation has become indispensable for maintaining the power system reliability. However, fault recovery planning for distribution network still faces challenges such as human error, redundant workflows, and duplicated work. Large language models (LLMs), which have exceptional semantic understanding and automated generation capabilities, have recently attracted more and more attention. In this paper, we propose a novel conversational agent based on the mainstream LLMs for fault recovery plan generation. Besides, we introduce a novel tool-learning method that integrates various functionalities, encompassing topology querying, power flow calculations, and formatted text generation. Experiments demonstrate that the fault recovery plan generation agent can effectively leverage the integrated tools, achieving an average success rate of 99.25% in tool invocation.

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.

Given the reverse distribution of energy supply and electricity demand in China, the Line-Commuted Converter High Voltage Direct Current (LCC-HVDC) delivery system has emerged as the primary solution to transmit renewable energy over long distances. The evaluation on potential damage of the wide-band oscillations to the grid stability requires the creation of a mathematical model of the LCC-HVDC delivery system to comprehensively analyze its interaction with renewable power stations. Traditionally, it has been assumed that the long DC transmission lines would separate the sending-end and receiving-end systems, leading to the simplification of the system into a single local rectifier station connected to a DC voltage source. This oversimplification, however, lacks a theoretical basis and hence, the scope of application of the equivalent model remains undefined. This study utilizes double Fourier transform to establish the impedance model of the LCC-HVDC delivery system, taking into account the receiving-end system which includes a remote inverter station and the AC grid. The internal harmonics distribution and propagation characteristics are then explored. Finally, the influence of the receiving-end AC grid on the complete system impedance is analyzed, providing insights into the dominant factors that determine the applicability of the equivalent model, which is limited to the rectifier station only.

Energy demand and supply vary from area to area, where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this article, we first model the EV charging network as a cyber–physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then, we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load-balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users’ behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, millions of extreme fast charging stations are expected to be established in the next decade in the U.S. However, the widespread of electric vehicles and extreme fast charging infrastructure would impact transportation systems, such as mobility, congestion, equity, toll revenue, and infrastructure maintenance needs. As transportation infrastructure can last for a long time, effective planning must be able to predict the impact of projects and policies decades into the future. Given the nature of transportation systems that is of multiple interacting systems, this study develops mathematical models to quantitatively analyze this complicated and important problem. In particular, we utilize game theory to understand the dependency between the choices made by travelers, and the congestion and delay in the system with respect to traffic assignment. Our research results provide insights and guidance to strategic planning of extreme fast charging infrastructure.

With the development of smart grid, the continuous updating of power grid data texts has increased the redundancy of the database, which has led to difficulties of filtering and obtaining information. Therefore, an intelligent recommendation method for power grid data is proposed, which combines deep learning to mark entities based on existing knowledge bases and knowledge graphs, and then uses intent prediction matching algorithms to deeply mine user search intentions to realize accurate prediction of user search intent. The experimental results show that the model has a good prediction effect, a wide range of applications, and effectively improves work efficiency.

Cloud-based workflow architecture has been widely used in e-science, e-business, smart city, and others, to automate business processes and improve their flexibility and maintainability. Online workflow executes in a collaborative and distributed environment and is prone to fraud and information leakage. Workflow authorization models are implemented to ensure that tasks are performed by authorized subjects with compliance of security/privacy polices. However, existing workflow authorization models have some limitations. First, most of the existing research focuses on static workflows, where an order arriving at a workflow traverses tasks in a fixed sequence. In many real applications, however, task routing is not deterministic, having a probability distribution or pattern that may be estimated from historical data. Second, existing research ignores practical resource constraints, like user utilization, order waiting time, etc. To address the limitations, this article studies the workflow authorization model under the more realistic dynamic settings. We formulate a workflow as a queueing system, so business constraints can be analytically represented, under reasonable assumptions. We model the studied problems as pseudo-Boolean satisfiaiblity problems and investigate their theoretical properties. We also develop algorithms and carry out computational studies. The experimental results show the effectiveness and efficiency of our developed solutions. Our research results are useful for production and process design in many real-life settings such as health care, online banking and electronic payment systems.

As a significant part of the next-generation smart grid, electric vehicles (EVs) are essential for most countries to achieve energy independence, secure energy supply, and alleviate the pressure on environmental protection and energy security. Although EVs have grown rapidly, the slow recharge time is still the biggest obstacle to a wider application. While gasoline vehicles can pump enough gasoline in less than ten minutes, which can carry themselves a few hundred miles. However, most of today’s fast-charging techniques take half an hour only to provide very limited miles of electric driving range.

Electric vehicles (EVs) are becoming increasingly popular, but the frequent charging and large charging latency remain major obstacles to the EV industry. This article focuses on the charging scheduling of on-the-move EVs in a transportation network to minimize EVs' charging latency, including driving time to charging stations (CSs), wait time and charging time. We formulate this charging scheduling problem as a graphical game to characterize the strong couplings of charging latency among neighboring EV players. Specially, we investigate correlated equilibrium (CE) to describe the joint strategies of EV players, which is expected to further reduce the charging latency of EVs compared with Nash equilibrium (NE). It is shown that CE always exists in a finite game, and can be found by linear programming tools. In addition, we propose a method of wait time prediction, which can improve the prediction accuracy by combining the data of deterministic EV arrivals and the stochastic property of potential EV arrivals. Simulation studies are used to examine the performance of the proposed game-based approach, the efficiency of CE, the preciseness of our proposed wait time prediction method, the impacts of CS deployment on EVs' charging latency, etc. We can draw a conclusion that our method has apparent advantages in situations where the locations of EV players are in dense manners.

The unmanned aerial vehicles (UAVs) formation needs the frequent data-exchanging of individual's state between units for monitoring and instruction uploading from the ground station, which inevitably occupies huge communication bandwidth. This paper presents the extended Kalman filter (EKF) design based on the event-triggered strategy to get UAVs greatly relieved of the communication burden with guaranteed accuracy. The event-triggered strategy firstly selects only the state measurements containing innovational information for the purpose of filtering. Due to the nonlinearity of UAV system, the EKF is further applied to make full use of the information from the prior event-trigger strategy so as to enhance the performance of estimation. The proposed algorithm is verified on the physical UAVs regarding the estimation quality and communication rate, demonstrating the robust dynamic performance with effectively reduced communication rate.

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

Content-based sensor search is a core application in the Internet of Things (IoT), where target sensors can be quickly found by predicting the current output of the sensors. Due to the lack of update mechanism, the established prediction model in existing search architectures will gradually become unavailable in the highly dynamic IoT environment. Hence, a dual prediction structure based on edge intelligence is proposed in this article, to maintain the performance of the prediction model with minimum communication cost in long-term prediction. To implement our architecture, a more effective online learning algorithm is proposed to update prediction models online combined with our proposed adaptive window pattern clustering (AWPC) algorithm. Meanwhile, based on the dual prediction scheme (DPS), a mechanism is deployed on edge sensors to achieve transfer decision making in our architecture, where edge computing is performed to achieve selectively reporting data. With the designed architecture, about 76.56% of the communication energy consumption could be saved while achieving a 95.47% average prediction accuracy in continuous long-term prediction.

In terms of preventing traffic accidents, improving traffic efficiency and ensuring personal safety, the research on vehicular ad hoc networks (VANETs) is of great significance. Message authentication is an important security foundation for VANETs. With the rapid growth of the number of access terminals, the existing computing power of the VANETs will not be able to meet the fast message verification service load of large-scale dynamic networks. This article proposes a novel distributed collaborative authentication method. By selecting a reasonable number of assistance verification terminals in the VANETs system and cooperating with the roadside unit (RSU) to jointly undertake the task of network message verification, the purpose is to reduce the verification delay and achieve fast message verification. The simulation results show that in a large-scale connected vehicle with a large number of system terminals, the system message verification delay of our scheme is shortened to one tenth of the centralized verification system message verification delay.

The theory of complex networks has been used in recent studies on robustness of power grids. In most of previous studies, however, the outage condition of nodes or links was not practical enough. Precisely, the outage of a node or link only depends on whether it is overloaded. While in real networks, a variety of factors such as aging of equipments or bad weather will also cause elements to fail. In this paper, we consider a probability failure model, which takes those factors into consideration. Then, we apply the model to study the robustness of power grids, and compare the results with those under the previous ideal model.

The wide area measurement systems (WAMS) plays a critical part in power system reliability. Among all the WAMS applications, the dynamic state estimation (DSE) gathers all the data from the phasor measurement units (PMU) through the communication link so that the real-time wide area monitoring is facilitated at the application server to capture the dynamics of every generators. However, the DSE inevitably suffers from the communication link congestion and latency due to the explosive increase of grid size. To deal with this problem, this paper designs the event-trigger cubature Kalman filter (ETCKF) as an effective solution to get the communication burden relieved, by transmitting only the PMU measurements containing innovation. In order to provide the design-oriented strategy, various kinds of event-trigger strategies are evaluated in view of the estimation performance under the same communication rate. Finally, the standard IEEE 39-bus system is under test to verify the feasibility of ETCKF and the performance of various strategies.

In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph, we investigate the problem of vulnerable line identification from a clustering perspective. By jointly considering the topological parameters and the electrical properties, we propose an affinity propagation-based bus clustering algorithm to classify buses into clusters, where the center of each cluster represents the most influential bus in each partition. According to the clustering results, we design a vulnerable line identification scheme, which captures different types of potential critical lines in the smart grid system. Experiments over the IEEE-39 bus system demonstrate the effectiveness and correctness of our proposed algorithm.

Mobile edge computing (MEC) in cyber-physical systems (CPSs) with massive resource-constrained edge computing node (ECN) faces new challenges in security provisioning. The traditional centralized security authentication schemes with low performance are no longer applied for MEC in CPS. Due to the mobility of ECN, it is extraordinarily practical for ECN to establish a security association with another AP once leaving the service area of its current AP. In this paper, we represent the related research and propose a novel and efficient software-defined networking (SDN)-based handover authentication scheme for MEC in CPS (SHAS). An authentication handover module (AHM) in the SDN controller is applied for key distribution and authentication management. Before ECN handovers, the AHM distributes a key to the current serving AP for ECN further handover. Whenever a handover happens, target AP requests the AHM for the one-time session key (OSK) to authenticate the ECN. The target AP and ECN can proceed with the 3-way handshake protocol by the OSK to achieve mutual authentication and secret key confidentiality. Using the logical derivation of Burrows, Abadi, and Needham and formal verification by automated validation of Internet security protocols and applications (AVISPAs), proposed SHAS scheme can get mutual authentication and secret key confidentiality with a strong anti-attack ability. The simulation results show that the SHAS scheme has the characteristics of lower computational delay and less communication resources. Finally, the practical demonstration of our scheme is done using the widely accepted NS-3 simulation.

The tensor product model transformation (TPMT) is an emerging numerical framework of the Takagi-Sugeno (T-S) fuzzy (or polytopic) system modeling for a linear matrix inequality based system control design. A nested TPMT (NTPMT) is proposed in this paper, which merges the dimensions of the tensors and performs the TPMT iteratively. The resultant fuzzy model is in a multilevel nested tensor product (TP) structure. The vertex tensor obtained by NTPMT has fewer dimension results than the original TPMT so the number of vertices or fuzzy rules, which have been the main bottleneck for further application of the TPMT in higher dimensional systems, is expected to decrease significantly. It is also proven that the NTPMT contains the hierarchical fuzzy logic, meaning that the NTPMT is capable of conducting hierarchical fuzzy modeling and reduction. Furthermore, because the inclusion of multiple TPMTs is prone to augment the conservativeness of the resultant fuzzy model, a suboptimal convex hull rectification algorithm for the TPMT is developed based on a newly defined tightness measure, and then extended to render the NTPMT as less conservative as possible. Finally, numerical simulations on two real physical systems (two- and four-parameter dimension) are verified to demonstrate the performance of the methods.

With rapid development and high penetration of renewable energy, distributed generation, and energy storage system, as well as the emerging of electric vehicles and responsive loads, numerous uncertainties are brought into modern power systems. The power system robust operation become much more complicated than before. So, there is a urgent demand to develop a "faster than real time" power system Energy Management System (EMS) analysis tool using advanced data management and analysis technologies. In this paper, the power system EMS real time analysis framework based on the evolving graph is proposed. The power system is modeled as an evolving graph and its network analysis is implemented on a graph database platform. Testing results with a provincial power system demonstrate that the proposed analysis framework can significantly improve the performance and reach the goal of real-time power system analysis.

With the rapid growth of electric vehicle (EV) ownership, attentions have been paid to the foundation of EVs, the electric vehicle supply equipment (EVSE). Different approaches of effort, among which battery swapping and fast charging are the two most well studied, have been made to solve the tradeoff problem between the battery charging speed and battery lifetime. There has been considerable debate over development strategy between charging and battery swapping. In passenger vehicles, the EV charging mode seems to dominate. But, does it mean that the battery swap mode is a dead-end? The answer should be “No”. There are use cases showing that battery swap can have great potentials for some particular uses, such as taxis and buses. This paper uses Monte Carlo simulations of vehicle behaviors to compare the service capacities and earnings of EV charging and battery swapping for both taxi and bus fleets. Stochastic models of taxis, buses, charging stations (CSs) and battery swapping systems are set up. Subsequently, service capacities of the EVSE are compared. The impact of factors on the service capacity, such as the size of the vehicle's battery, vehicle's moving speed, the power of the CS, and the price of the swapping service is investigated. Finally, possible reasons of today's less prevalence of battery swapping stations are discussed. The results of the analysis, which can be helpful to policymakers and industry investors, show that with same service capacity, an EV battery swapping station could provide significantly more financial and social benefits for the vehicle operators and EVSE service providers than that of an EV CS.

Three-phase inverter is a very important interface in microgrid. Droop control and virtual impedance are widely used to improve the power sharing capability and stability of such system, which however becomes ineffective under unbalanced conditions and may even cause potential stability problems. This paper proposes a fully self-adjusted virtual impedance design to guarantee the decoupling of active and reactive power. The arbitrary unbalanced information is acquired through the pseudo-inverse impedance matrix modeled by full-dq technique. Based on the unbalanced impedance identified, the adaptive droop control is achieved for not only accurate power sharing but also reliable voltage support with autonomous unbalanced voltage compensation. Simulations results verify the accurate identification of unbalanced impedance and effectiveness of the proposed method on the unbalanced compensation.

Plenty of distributed generation (DG) units directly export DC power such as the PV arrays. As a commercial interface, three-phase voltage-source inverters (VSIs) are commonly equipped for the power conversion from the DC power sources to the AC utility. In a practical DG system, the grid-connected loads are full of dynamics at the point of common connection (PCC). Thus, grid-connected loads inevitably have mutual interaction with VSI at PCC, which may have an impact on the stability and deteriorate the regulation capability of VSI. In this paper, the oscillatory behavior of single three-phase VSI is studied in the presence of resistive loads. The corresponding oscillation boundaries of the system are numerically collected for various circuit conditions with respect to the emergence of a low-frequency oscillation as a Hopf-type phenomenon.

DFIG wind turbine systems are attractive renewable resources in distributed generation units and micro-grids. Due to the direct-connection of its stator windings to the single point of common coupling, DFIG is vulnerable to the unequally-distributed loads at low voltage and arises various hazardous complex dynamic behavior in reliability and harmonics. This paper studies the dynamic behavior of this system and reveals the period-doubling bifurcation phenomena. Under certain loading conditions when the system is undergoing negative sequences from unbalanced loads, the period-doubling and its stability boundaries has been observed and investigated with full-circuit MatLab simulations, illustrating the potential instability problems from loads and control parameters.

As the advancement of sensing and networking technologies, participatory sensing has raised more and more attention as it provides a promising way enabling public and professional users to gather and analyze private data to understand the world. However, in these participatory sensing applications both data at the individuals and analysis results obtained at the users are usually private and sensitive to be disclosed, e.g., locations, salaries, utility usage, consumptions, behaviors, etc. A natural question, also an important but challenging problem is how to keep both participants and users data privacy while still producing the best analysis to explain a phenomenon. In this paper, we have addressed this issue and proposed M-PERM, a mutual privacy preserving regression modeling approach. Particularly, we launch a series of data transformation and aggregation operations at the participatory nodes, the clusters, and the user. During regression model fitting, we provide a new way for model fitting without any need of the original private data or the exact knowledge of the model expression. To evaluate our approach, we conduct both theoretical analysis and simulation study. The evaluation results show that the proposed approach produces exactly the same best model as if the original private data were used without leakage of the fitted model to any participatory nodes, which is a significant advance compared with the existing approaches [1-5]. It is also shown that the data gathering design is able to reach maximum privacy protection under certain conditions and be robust against collusion attack. Furthermore, compared with existing works under the same context (e.g., [1-5]), to our best knowledge it is the first work showing that not only the model coefficients estimation but also a series of regression analysis and model selection methods are reachable in mutual privacy preserving data analysis scenarios such as participatory sensing.

It has been observed that the TCP-RED system may exhibit instability and oscillatory behavior. Control methods proposed in the past have been based on the analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA has been used for detecting long-range correlations in seemingly non-stationary noisy signals. The key indicator emanating from DFA is known as the scaling exponent. By examining the variations of the DFA scaling exponent when varying system parameters, we quantify the stability of the TCP-RED system in terms of system’s characteristics.

It has been observed that a bottleneck random early detection (RED) gateway becomes oscillatory when regulating a flow in multiple TCP connections. The stability boundary of the TCP-RED system depends on various network parameters, making the adjustment of the RED gateway a difficult task. Based on a fluid-flow model, analytical conditions were formulated that describe the stable boundary of the RED gateway depending on the number of TCP Reno connections. The proposed model accurately generates a stability boundary surface in a four dimensional space, which facilitates the adjustment of parameters for stable operation of the RED gateway. The accuracy of the analytical results has been verified using the ns-2 network simulations.