Network Operation Research Articles

Research on optimal scheduling strategy of source-grid-load-storage in rural low-voltage distribution network

The increasing demand for centralised power consumption in rural agriculture may result in low voltage and three-phase imbalance issues at the end of the distribution network. Furthermore, the extensive implementation of distributed photovoltaic (PV) systems may cause an imbalance between agricultural power load and PV output, exacerbating the issue of PV losses. In this paper, an optimal scheduling strategy for an integrated source-networkload- storage system is proposed to solve the above problems. The proposed strategy involves a dispatch model that includes PV power generation and energy storage, with the objective of maximising PV consumption and minimising operating costs. The PPO algorithm is finally employed to solve the cooperative optimisation model for source-grid-load-storage. The proposed scheduling strategy has been validated through examples, and the results demonstrate its effectiveness in ensuring the economic and safe operation of rural distribution networks.

Operation optimization of large-scale natural gas pipeline networks based on intelligent algorithm

With the expansion of natural gas pipelines into larger networks, there is a growing concern regarding energy consumption in the operations of pipeline systems. However, the intricate network structure and hydraulic properties pose challenges for operation optimization. In this paper, a Mixed Integer Nonlinear Programming model is proposed to optimize the steady-state operation of natural gas pipeline network, aiming to minimize the energy consumption of compressor units. The model incorporates constraints related to flow direction, flow balance, pressure drop in pipeline sections, and pressure increase at compressor stations. To solve the complex nonlinear model efficiently, a stochastic optimization algorithm that integrates particle swarm optimization and high-fidelity simulation is proposed. The case study is conducted on a large-scale natural gas pipeline network consisting of forty-three pipeline sections and four compressor stations. The optimal operation scheme is calculated, and the outlet pressures of each compressor are determined. The results demonstrate that the stochastic optimization algorithm proposed in this paper can reduce energy consumption by 21.23 % at most and 19.77 % on average during pipeline operation, which can provide guidance for the operation management of large-scale natural gas pipeline network.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground fault

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground faultTHE PURPOSE. Develop a laboratory rig for testing parallel arc fault and spark gap protection devices for tripping in the event of a ground fault. Conduct laboratory rig tests with subsequent development of requirements for the use of parallel arc fault and spark gap protection devices for tripping in the event of a ground fault.METHODS. Mathematical and statistical methods of analysis were used to achieve the stated objective. Laboratory tests of parallel arc fault and spark gap protection devices for different network parameters for correct tripping in the event of a ground fault.RESULTS. Laboratory tests of parallel arc fault and spark gap protection devices for correct tripping in the event of a ground fault were conducted with different network parameters. The oscilloscope recorded the shape of the voltage and current curve in the event of a ground fault and the tripping time of the devices. The tested devices demonstrated the ability to recognize and disconnect a ground fault. The tripping time of the devices varies, however, does not exceed the established requirements.CONCLUSION. A laboratory stand has been developed for testing parallel arc breakdown and spark gap protection devices for operation during a ground fault. This stand makes it possible to test devices for adequate operation during a ground fault with subsequent certification. The tests conducted demonstrate the need to develop mandatory requirements for the device to standardize their operating algorithm. Therefore, the use of this protection device can contribute to safer operation of low-voltage electrical networks by preventing emergency situations and reducing the risk of fires. This is an important technical aspect for improving the reliability and safety of power supply.

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

Coordinated active-reactive power optimization considering photovoltaic abandon based on second order cone programming in active distribution networks

On the basis of predecessors’ coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs.

RSE-YOLOv8: An Algorithm for Underwater Biological Target Detection.

Underwater target detection is of great significance in underwater ecological assessment and resource development. To better protect the environment and optimize the development of underwater resources, we propose a new underwater target detection model with several innovations based on the YOLOv8 framework. Firstly, the SAConv convolutional operation is introduced to redesign C2f, the core module of YOLOv8, to enhance the network's feature extraction capability for targets of different scales. Secondly, we propose the RFESEConv convolution module instead of the conventional convolution operation in neural networks to cope with the degradation of image channel information in underwater images caused by light refraction and reflection. Finally, we propose an ESPPF module to further enhance the model's multi-scale feature extraction efficiency. Simultaneously, the overall parameters of the model are reduced. Compared to the baseline model, the proposed one demonstrates superior advantages when deployed on underwater devices with limited computational resources. The experimental results show that we have achieved significant detection accuracy on the underwater dataset, with an mAP@50 of 78% and an mAP@50:95 of 43.4%. Both indicators are 2.1% higher compared to the baseline models. Additionally, the proposed model demonstrates superior performance on other datasets, showcasing its strong generalization capability and robustness. This research provides new ideas and methods for underwater target detection and holds important application value.

Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks.

Implementation of optimal routing in heterogeneous wireless sensor network with multi‐channel Media Access Control protocol using Enhanced Henry Gas Solubility Optimizer

SummaryThe majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Inclusion of water age in conjunctive optimal operation of water and power networks

ABSTRACT Water distribution systems (WDSs) are vital infrastructures designed to deliver water safely to consumers. This complex system necessitates continuous operational decisions, often optimized for efficiency. WDSs rely on power grids (PGs) to operate pumps and treatment facilities. PGs, likewise crucial, require strategic management to meet demand and environmental standards. Integrating the operation of both systems has garnered attention for its potential cost, energy, and environmental benefits. By leveraging the interconnection, trade-offs can be assessed, leading to improved solutions. Past research primarily focused on cost or carbon emissions as well as on hydraulic and voltage constraints. However, this study examines the impact of power systems on water quality, particularly water age, a key operational concern. Incorporating PGs into the optimal operation problem may alter flow directions, thereby influencing water age. Through mathematical modelling, this study evaluates these effects and applies them to simple case studies, demonstrating the influence of PG operation on water quality. Results demonstrate how tank constraints affect water age and show that a conjunctive operation approach, although beneficial for reduction of cost and energy consumption, can be damaging for water quality.

Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.

Operation of spinal sensorimotor circuits controlling phase durations during tied-belt and split-belt locomotion after a lateral thoracic hemisection.

Locomotion is controlled by spinal circuits that interact with supraspinal drives and sensory feedback from the limbs. These sensorimotor interactions are disrupted following spinal cord injury. The thoracic lateral hemisection represents an experimental model of an incomplete spinal cord injury, where connections between the brain and spinal cord are abolished on one side of the cord. To investigate the effects of such an injury on the operation of the spinal locomotor network, we used our computational model of cat locomotion recently published in eLife (Rybak et al., 2024) to investigate and predict changes in cycle and phase durations following a thoracic lateral hemisection during treadmill locomotion in tied-belt (equal left-right speeds) and split-belt (unequal left-right speeds) conditions. In our simulations, the "hemisection" was always applied to the right side. Based on our model, we hypothesized that following hemisection, the contralesional ("intact", left) side of the spinal network is mostly controlled by supraspinal drives, whereas the ipsilesional ("hemisected", right) side is mostly controlled by somatosensory feedback. We then compared the simulated results with those obtained during experiments in adult cats before and after a mid-thoracic lateral hemisection on the right side in the same locomotor conditions. Our experimental results confirmed many effects of hemisection on cat locomotion predicted by our simulations. We show that having the ipsilesional hindlimb step on the slow belt, but not the fast belt, during split-belt locomotion substantially reduces the effects of lateral hemisection. The model provides explanations for changes in temporal characteristics of hindlimb locomotion following hemisection based on altered interactions between spinal circuits, supraspinal drives, and somatosensory feedback.

Design, development and multi-scenario application of portable multifunctional test master based on μM-PMU: The case study of Lingang new City Grid, Shanghai

With the large-scale access of new energy generation and electric vehicle charging piles to the grids, power generation and loads are characterized by random fluctuations and intermittency, which affect the stable operation of the distribution network. μM-PMU is capable of realizing the dynamic process monitoring of the grids. In order to solve the function and performance testing problems of multiple application scenarios of μM-PMU, this thesis develops a portable master station based on synchronous phasor measurement system to realize zero drift, voltage amplitude measurement accuracy, voltage phase angle accuracy, current amplitude measurement accuracy, current phase angle measurement accuracy, frequency measurement accuracy, voltage amplitude change with frequency accuracy, and voltage phase angle change with frequency accuracy, Accuracy of current amplitude with frequency change, accuracy of current phase angle with frequency change, active power accuracy, reactive power accuracy, amplitude step response, phase angle step response, frequency step response, a total of 15 test items. It can automatically calculate test results and generate test reports according to different equipment models, reducing the mistakes and calculation errors that may be caused by manually recording test data and improving the accuracy of test results and calculation precision.

Hierarchical Glocal Attention Pooling for Graph Classification

Graph pooling is an essential operation in Graph Neural Networks that reduces the size of an input graph while preserving its core structural properties. Existing pooling methods find a compressed representation considering the Global Topological Structures (e.g., cliques, stars, clusters) or Local information at node level (e.g., top-k informative nodes). However, an effective graph pooling method does not hierarchically integrate both Global and Local graph properties. To this end, we propose a dual-fold Hierarchical Global Local Attention Pooling (HGLA-Pool) layer that exploits the aforementioned graph properties, generating more robust graph representations. Exhaustive experiments on nine publicly available graph classification benchmarks under standard metrics show that HGLA-Pool significantly outperforms eleven state-of-the-art models on seven datasets while being on par for the remaining two.

Optimal Clean Energy Resource Allocation in Balanced and Unbalanced Operation of Sustainable Electrical Energy Distribution Networks

Optimal Clean Energy Resource Allocation in Balanced and Unbalanced Operation of Sustainable Electrical Energy Distribution Networks

Integrated urban wastewater system operation: Performance evaluation and sewer network's operation implications

ABSTRACT The paper deals with the operation of the Integrated Urban Wastewater System as composed of the Sewer Network and associated Wastewater Treatment Plant. The performance of the integrated system is defined in terms of appropriate, selected, performance indicators of both subsystems. Both subsystems are connected and interact by a piping system. It is shown how the performance of the Integrated Urban Wastewater System is affected by various Sewer Network control scenarios: (1) only the Sewer Network outlet valve opening is controlled, (2) all the Sewer Network valves are controlled by a Model Predictive Control algorithm developed for minimizing the environmental impact of the subsystem and modified by constraining it in terms of Sewer Network outlet valve opening. More specific, it is proven that the Sewer Network outlet valve opening has an important role in the performances of the integrated system, and that already existing Sewer Network control strategies can be altered in such a way that they will enhance these performances.

Design of a 240 elements antenna system for 5G massive MIMO applications with an inclined beam and two operating modes

A massive MIMO (multiple-input-multiple-output) antenna system for 5G base stations is developed, featuring 240 elements and 60 ports. The system supports two modes: individual port operation and network operation. It has a rectangular shape with dimensions of 28×16.8×0.32 cm³ and consists of three layers per side, with 15 ports per layer. Each port contains a 2×2 patch sub-array operating at 3.5 GHz, with beam tilting to generate uncorrelated radiation patterns. The system provides a measured bandwidth of 252 MHz, covering the 3.39–3.64 GHz band. Gain and envelope correlation coefficients are evaluated according to mMIMO parameters. A prototype is fabricated, and the results obtained through CST simulations are validated by experimental measurements.

Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks.