Node Load Research Articles

Improving the quality of the power supply system of technical fleet vessels when supplied from shore regional electric power systems

A significant increase in the load on technical fleet objects, such as floating cranes, dredgers, dredgers, etc., causes the problem of electromagnetic compatibility. Electricity supply of technical fleet vessels is carried out from 0.4 kV shore networks for economic reasons, but the quality of electricity of the shore-to-ship power transmission does not meet the requirements of GOST 32144-2013 and the Rules for the Classification and Construction of Inland Navigation Vessels of the Russian River Register (RSRR). Physical wear and tear of technical fleet vessels exacerbates this problem. Subject of the study: processes affecting the stability of the electrical load on the voltage of the ship's 0.4 kV network when supplying the vessel with electricity from a 10 kV shore network and violating the EMC of technical equipment. Objective of the study: development of scientific provisions and recommendations to improve the stability of ship voltage load nodes when powered from shore networks. Research subject: power supply systems of technical fleet vessels when powered from shore power systems. Research results: based on the conducted research, it is shown that the proposed method increases the stability of the electrical load on the voltage of technical fleet vessels when powered from shore networks, which is essential for water transport.

On-demand routing algorithm for multipath selection based on node load states in mobile ad hoc networks

On-demand routing algorithm for multipath selection based on node load states in mobile ad hoc networks

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN

Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN’s hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network’s data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN’s legacy ADR scheme for a range of application data intervals.

Research on the Robustness of Command and Control Networks under Cascading Failures

The current analysis of cascading failures in command and control networks pays little attention to their roles and mechanisms, resulting in challenges in quantifying survivability evaluation metrics and limiting practical application. To address these issues, this paper designs a command and control network model with a recovery strategy to improve the scientific evaluation of critical nodes and enhance the reliability of subsequent cascading failure simulations. Two capacity parameters are introduced to analyze the nonlinear behavior between network node load and capacity, and an optimal recovery strategy is proposed. This strategy prioritizes the recovery of critical nodes, thereby minimizing the overall probability of network failure. Simulations were conducted under both random failure and deliberate attack scenarios, comparing the proposed strategy with random recovery and betweenness-priority recovery strategies to identify the optimal recovery approach. The experiments showed that the optimal recovery strategy significantly enhanced the network’s survivability and recovery efficiency, allowing for the restoration of basic network functions in the shortest possible time and reducing the impact of cascading failures. By integrating the operability and uncertainty of real-world command and control networks, this method improved the network’s recovery capability and overall stability in the face of cascading failures through scientific evaluation and strategy optimization.

Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency.

Research on mobile energy storage scheduling strategy for emergency power supply protection of post-disaster isolated loads

Aiming at the problem of insufficient power supply capacity of isolated loads in oceanic islands, a concept based on mobile energy storage and power conservation is proposed in this paper. Firstly, the energy flow and traffic flow characteristics of mobile energy storage are quantitatively analyzed, and an accurate mathematical model is established. Furthermore, the time-space distribution characteristics of load are carefully considered, and the Voronoi diagram and Tyson polygon method are introduced to determine cell differentiation. On this basis, combined with the power demand of load nodes and the energy storage characteristics of mobile energy storage vehicles, the evaluation indicators of cell energy and relative surplus energy of cells are designed, and the agent-cellular automata algorithm is used to schedule the mobile energy storage. The simulation results show that the power supply mode based on mobile energy storage can effectively improve the reliability of isolated loads. This paper provides a new perspective for solving the reliable power supply problem of isolated loads.

Building electricity load forecasting based on spatiotemporal correlation and electricity consumption behavior information

Accurate prediction of building electricity load is essential for grid management and building optimization operations. This paper proposes a novel approach based on spatiotemporal correlations and electricity consumption behavior information. The K-Medoids algorithm and the Derivative Dynamic Time Warping (DDTW) distance are employed to explore the correlation between electricity consumption behaviors among different partitions and floors. Different partitions and floors are clustered and grouped, followed by modifying the adjacency matrix with electricity consumption behaviors. The hybrid model and K-Medoids-LSTM model are proposed separately for clusterable nodes and non-clustered nodes. For clusterable nodes, spatial-temporal features are extracted, trained, and predicted with the hybrid model based on graph neural networks (GNNs) and LSTM models. A K-Medoids-LSTM model based on the K-Medoids algorithm is proposed to predict the electricity load of the non-clustered nodes. To explore the model's practicality, we predicted the building electrical load under different dataset sizes. The model achieves an R2 above 0.89, and the MAE, MSE, and RMSE of the GCN-LSTM and GAT-LSTM models all remain below 0.1, indicating strong predictive capabilities. The results demonstrate that, without relying on other external features, the proposed method can accurately predict the building electricity load for different partitions and floors simultaneously.

Damage identification of statically indeterminate trusses based on Bayesian updating

Amidst China's rapid development, most structures and edifices have advanced into the mid-service life phase, where components and frameworks gradually undergo aging and deterioration. Ensuring the safety and functionality of buildings necessitates a heightened emphasis on structural health assessments. This study aims to explore a more cost-effective and time-efficient diagnostic strategy for detecting damage in statically indeterminate trusses, while exploring techniques to uphold data precision by reducing loading nodes and measurement parameters. Employing OpenSees for model construction, this paper utilizes Bayesian updating through Monte Carlo rejection sampling to pinpoint damage in statically indeterminate trusses. Moreover, it unveils the fundamental principles behind optimal loading and measurement strategies. Findings from conducted experiments advocate for a comprehensive approach integrating displacement measurements across multiple axes for enhanced accuracy, surpassing single-direction measurements. Validation of the virtual work principle underscores this method's efficacy. Ultimately, this methodology holds promise for curbing engineering expenses and amplifying operational efficiency in real scenarios.

Generalized load modeling approach considering multiple distributed generation integration

To solve the problem of node characteristic uncertainty caused by the connection of multiple distributed generations to the original load node, a new method for modeling the steady-state characteristics of generalized load nodes based on scene clustering was proposed. Scenario clustering takes into account three factors: wind farm output fluctuation, photovoltaic output fluctuation, and demand-side power load fluctuation, and extracts typical scenarios of generalized load data. According to the uncertainty change of node characteristics in different scenarios, the clustering data is segmented and refined, and its probability distribution is counted. Clustering information, probability information, and maximum value are used as three types of features of generalized load and are custom encoded as 12-dimensional feature vectors as neural network inputs. To improve the identification performance of generalized load features, the back propagation (BP) dichotomy neural network is used to build a unified model of node features. The simulation results show that the proposed method can not only model accurately but also introduce probability information through statistical data samples, which can provide an auxiliary reference for the prediction and regulation of generalized loads.

Modernization of the method for predicting energy consumption of minigrid load nodes by using extrapolation of the trend line obtained using wavelet transformation of the electrical load graph

Modernization of the method for predicting energy consumption of minigrid load nodes by using extrapolation of the trend line obtained using wavelet transformation of the electrical load graph

Optimized Genetic Algorithms: Study of the Impact on Load Distribution in the Power System

In order to balance the power flow, voltage and current between renewable energy power generation and thermal power generation, this paper takes renewable energy as the research object, analyzes the power flow and voltage after grid connection, and proposes an improved genetic algorithm for research. Firstly, the log function is used to analyze the power flow and voltage data before and after grid connection, reduce the complexity of the data, and calculate the proportion of photovoltaic energy. Then, the load analysis of the power system was carried out by genetic iteration to identify the overloaded nodes, and the identification standard was greater than 80% of the rated load. Finally, a load set is formed, and information such as changes, power flow, and voltage of different power generation nodes is recorded. The results show that the genetic algorithm can maximize the proportion of renewable energy to 50% and reduce the power flow change rate of the power system to 10%. At the same time, improve the load efficiency of the power system, so that the node load is between 75~80%, average the load of each node, especially the photovoltaic matrix and fan, and shorten the load deployment time, so that it is 10s smaller. Compared with the previous algorithms, the genetic algorithm proposed in this paper is better, which can meet the load distribution requirements of the power system and expand the energy proportion of renewable energy.

Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Efficient Workflow Scheduling in Edge Cloud-Enabled Space-Air-Ground-Integrated Information Systems

To address the challenges posed by the dynamism, high latency, and resource scarcity in integrated air-space-ground hybrid edge cloud environments on task completion times and node load, we designed a task scheduling system for scenarios involving the transmission and processing of interdependent tasks. This system integrates a graph neural network with attention mechanism and deep reinforcement learning. Specifically, we employ a graph encoder to extract features from DAG tasks and resources. Task scheduling solutions for dynamic environments are then generated using attention mechanism-equipped graph decoder, which are subsequently optimized based on performance metrics through the use of an Advantage Actor-Critic algorithm. Experimental results indicate that this algorithm performs well in terms of completion time and node load balance across tasks with different workflow structures, demonstrating its adaptability to highly dynamic edge cloud environments.

Construction of the structure of rural electric networks taking into account the availability of renewable sources of electricity

It is noted that although the share of renewable electricity sources continues to grow and develop rapidly, the industry still faces many challenges, including how to continue to reduce electricity losses and annualized costs, improve operation and maintenance efficiency, maintain grid stability, ensure safety and reliability of the power supply system containing renewable sources of electrical energy. It is shown that an effective solution to these problems is possible only on the basis of the analysis of the prospects for the development of local electric power systems that contain renewable sources of electric energy, the development of technical and organizational support mechanisms that will contribute to the construction of modern system (schematic) solutions. It is proposed to apply the potential surface method for building the optimal structure of the rural electrical network during its design and modernization in the presence of renewable sources of electrical energy, which allows optimizing the structure of the network from the point of view of electrical energy losses and reducing annualized costs. The algorithm for forming the structure of power supply systems is described, which implements the simultaneous solution of the tasks of determining the number of load nodes, distributing electrical receivers between them, determining the design of power sources, taking into account the discreteness of the design of system elements. The results of numerical modeling based on the proposed algorithm are presented on the example of solving the problem of reconstruction of a section of the rural electricity network.

A Bayesian Hierarchical Model to create synthetic Power Distribution Systems

The growing complexity of Power Distribution Systems, driven by distributed generation, renewable energy integration, and increasing demand, has led to restricted access to DS data due to security and privacy concerns. This study addresses limited data accessibility by proposing a hybrid approach for crafting synthetic power distribution systems tailored for power system analysis and control. Synthetic power distribution systems refer to artificially generated models that faithfully replicate real-world DS features while upholding security and privacy constraints. This innovative methodology merges a Bayesian Hierarchical Model with Markov Chain Monte Carlo techniques, utilizing georeferenced data to capture intricate system dependencies, feeder configurations, switch statuses, and load node distributions. Leveraging OpenStreetMaps for DS topology, the approach incorporates expert knowledge and real-world data. Results highlight the methodology’s ability to evaluate credible intervals for parameters, facilitating a probabilistic assessment of uncertainties and enhancing decision support in power system analysis and control. Findings affirm the hybrid approach’s efficacy in generating realistic synthetic DSs, bridging the gap between statistical and georeferenced methodologies for advanced power system analysis and control. The capacity to generate synthetic DSs provides valuable insights into power system dynamics, addressing security, privacy, and data accessibility concerns for a more informed decision-making process.

MATHEMATICAL MODEL AND ALGORITHM FOR THE DETERMINATION OF THE ORIGIN OF ELECTRICITY FROM RENEWABLE ENERGY SOURCES IN THE ELECTRIC POWER SYSTEM

The article deals with the development of a method and algorithm for calculation of individual compo-nents of electricity flows in the branches of an electric power grid circuit caused by generation and con-sumption in its nodes. In particular, it is about estimating the share of renewable energy sources (RES) in the electricity consumption of a given consumer. The method is based on methods and algorithms for the calculation of steady-state modes of power grids, integrating a mathematical model for determining the components of current flows in the branches of the power system circuit. The model employs distribution coefficients of currents in the circuit branches from RES nodes and node voltages to form a matrix of cur-rent distribution coefficients for RES across the power grid branches. Since RES in electric power systems (EPS) use general power grids to transmit the electricity they generate, accurate determination of their power share within overall flows enables the impact of RES on the EPS mode parameters to be considered. The peculiarity of the algorithm for estimating the share of RES in the electricity consumption of a given consumer is its consideration of physical processes in the power system. The input data for the calculation are the power system parameters, the node loads (specified by capacity or schedules), as well as arrays of RES generation and centralized power supply nodes and consumers with a controlled value of the Guar-anteed Origin of electricity from RES. The voltages during the formation of the matrix of power distribu-tion coefficients at nodes along the branches of the power grid are determined by the calculation results of the power grid steady-state modes or by experimental measurement data. The method's efficacy and the accuracy of estimating the RES share in the power consumption of a given consumer are verified and demonstrated through a practical example. Determination of the share of the consumer's electricity con-sumption, which origin from renewable sources can be guaranteed, creates an enabling environment that would foster producers and consumers to increase the use of renewable energy sources and promotes the development of the green energy market. Ref. 18. Fig.3.

Identification of Key Risk Nodes and Invulnerability Analysis of Construction Supply Chain Networks

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.