Supply Nodes Research Articles

The research of knowledge diffusion network model for Tourism Destination-Public ecological civilization.

Visitor education plays a crucial role in the knowledge diffusion process in outdoor recreation and nature-based tourism. It entails sharing information, experiences, and insights with visitors to enhance their understanding and appreciation of the natural environment. Our methodology for investigating the diffusion of ecological civilization knowledge in tourism destinations involves constructing a knowledge diffusion network model. In this model, scenic spots, tourists, and the public are defined as network nodes, with the communication channels between them representing the edges of the network. By constructing a scale-free complex network, the knowledge diffusion mode of scenic spots can be depicted. The layer of resource supply node consists of different scenic spots, forming the core nodes set for knowledge diffusion in the tourism industry. This research aims to further explore the social and economic value of the tourism areas' ecological civilization knowledge diffusion, as well as analyze the path, quantity, and cost of knowledge diffusion. by analyzing this knowledge diffusion network model, insights into the effectiveness and impact of visitor education in promoting ecological civilization and sustainable practices in tourism destinations can be gained. Overall, this approach provides a theoretical framework for investigating and comprehending the knowledge diffusion process in Tourism Destination-Public ecological civilization, thereby shedding light on the social and economic benefits that can be derived from sustainable tourism practices.

Structural characterisation for the synthesis of large‐scale combined electric–gas networks

AbstractIn this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled‐infrastructure analysis with large‐scale realistic test cases that do not contain non‐public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering‐based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.

Light energy harvested flexible wireless sensing for disinfection sterilization in food storage

Cold chain logistics involves all aspects of food storage, transport, and sales. This study focuses on innovating cold storage disinfection within this context. Regular disinfection reduces microorganism growth, improves food quality, and ensures safety. Traditional methods lack automation, accurate monitoring, and responsive control. Conventional sensors are unsuitable for curved bottles and need frequent power changes. This paper proposes a light energy harvested flexible wireless sensing for disinfection sterilization in food storage (FLWDS). FLWDS comprises five parts: light energy harvesting and power supply node (LEPN), sensing node, control node, monitoring node, and host computer. A predictive control model improves response speed and accuracy. Bluetooth technology ensures real-time communication between nodes. FLWDS collects light energy inside the cold store as a supplementary energy source and can be used in curved structures with a response time of less than 1 s. The maximum communication distance is 40 m. The flexible level sensing system can be designed according to the induction pad’s length, with a measurement accuracy of 0.3 mm, sampling every 4.8 ms, and a resolution of 0.2 mm. FLWDS provides self-powered flexible wireless sensing, ensuring accurate liquid level measurement, improved food quality, reduced pollution, and increased sustainability.

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.

Smart management of pressure regulating stations to maximize hydrogen injection in a gas distribution network

Gas distribution networks traditionally maintain constant pressure setpoints at regulating stations, ensuring a consistent supply to end-users with a high safety margin. However, the prospect of hydrogen injection in the gas networks necessitates rethinking operational strategies. This study examines the environmental and fluid-dynamic implications of implementing a smart pressure management approach in regulating stations within a distribution network with a localized hydrogen injection. The research introduces a novel simulation-based optimization algorithm, incorporating a steady-state fluid-dynamic model with hydrogen tracking, used to optimize the pressure setpoints in the stations. The algorithm is applied to a two-pressure levels looped gas network with multiple gas supply nodes and a single hydrogen injection. The test case results reveal that utilizing dynamic pressure setpoints leads to a 22% increase in hydrogen penetration compared to standard operation. Therefore, this study highlights that smart pressure modulation in a gas network with hydrogen injection can unlock a latent decarbonization potential, while simultaneously adhering to safety limits.

Performance analysis of routing protocols for an efficient data transmission in 5G WSN communication

In WSN structures the routing scheme the usage of the sensor nodes are carried out in between group of specific clusters. The nodes are working for information aggregation from these supply nodes they also performs statistics dissemination and community management and activities sensing and records gathering in the neighborhood. Many clustering topology are proposed in recent years to localize the route inside the cluster. In this paper we have reviewed and in contrast these topologies to locate out the network mechanism which are less difficult to control and scalable for getting excessive satisfactory response with recognize to dynamics of the environment.

A Novel Skip Orthogonal List for Dynamic Optimal Transport Problem

Optimal transport is a fundamental topic that has attracted a great amount of attention from the optimization community in the past decades. In this paper, we consider an interesting discrete dynamic optimal transport problem: can we efficiently update the optimal transport plan when the weights or the locations of the data points change? This problem is naturally motivated by several applications in machine learning. For example, we often need to compute the optimal transport cost between two different data sets; if some changes happen to a few data points, should we re-compute the high complexity cost function or update the cost by some efficient dynamic data structure? We are aware that several dynamic maximum flow algorithms have been proposed before, however, the research on dynamic minimum cost flow problem is still quite limited, to the best of our knowledge. We propose a novel 2D Skip Orthogonal List together with some dynamic tree techniques. Although our algorithm is based on the conventional simplex method, it can efficiently find the variable to pivot within expected O(1) time, and complete each pivoting operation within expected O(|V|) time where V is the set of all supply and demand nodes. Since dynamic modifications typically do not introduce significant changes, our algorithm requires only a few simplex iterations in practice. So our algorithm is more efficient than re-computing the optimal transport cost that needs at least one traversal over all |E|=O(|V|^2) variables, where |E| denotes the number of edges in the network. Our experiments demonstrate that our algorithm significantly outperforms existing algorithms in the dynamic scenarios.

Allocation of power losses and energy in the distribution network

The goal is to determine methods for calculating power losses in a three-phase four-wire low voltage distribution network using measurements of a balance smart meter and consumer smart meters, and to establish the factors influencing the power losses and their allocation among individual network wires, loads, and consumers. The study involved examining three methods for determining power losses for current measurement snapshot. The first method suggests calculating losses as the difference between the power supplied to the network and the total power consumed. The second method calculates power losses using the contribution method. The third method, which in addition to measurement information requires knowledge of the topology and parameters of the network components, determines power losses based on the results of the state estimation method. The research proposes an algorithm for transition from a four-wire distribution network modeling to a three-wire one. The algorithm allocates power losses of the neutral wire among the phase wires. The findings indicate that the negative losses in the network with unbalanced phase loads are caused by the presence at the nodes of the least loaded phase of higher voltage than the voltage at the power supply node. The reason for higher losses in phases with minimal load is the uneven allocation of loads in the phases. In addition, the study reveals that the power loss values obtained by the contribution method, i.e. directly from the measurements of smart meters, are closer to the losses determined from the readings of the balance meter and consumer meters, compared to the losses found from the state estimation results. The considered methods for calculation and allocation of power losses are illustrated by an example of a real-world distribution network equipped with smart meters. The paper demonstrates the examples of allocating total power losses between phase wires and a neutral wire, among phase wires only, and between total loads at phase nodes and individual consumers in phases.

Evaluation of existing underground public space supply based on the Node-Place-Experience model

The development of underground public space is considered to be a major contributor to urban sustainability. However due to the lack of customized tools for identification and assessment the effective or ineffective supply of existing underground public space has yet to receive sufficient attention resulting in frequent problems such as its undersupply and underutilization of space. Therefore this study proposes three assessment frameworks for existing underground public spaces namely traffic supply (Node) land use and activity diversity (Place) and user experience (Experience) to judge the effectiveness of the supply in 13 cases in the main urban area of Chongqing by the degree of balance of the three dimensions, and to categorize the results of the evaluation through hierarchical clustering. The study confirms that except for Nanping the other cases suffer from under-provision especially in terms of transport provision such as the low connectivity of the existing underground public space to other urban spaces. Afterward we classified the evaluated cases into four clusters: “unsustainable place with potential to be balanced,” “developing balanced with promising potential to be balanced,” “developing balanced with potential to be balanced,” “developing balanced with lower potential to be balanced” and proposed corresponding optimization paths. Our method can be used as an effective tool to identify and measure the effectiveness of existing underground public space supply and to provide a database and decision support for the optimization of future renovation of the project.

A social-ecological network approach to quantify the supply-demand-flow of grain ecosystem service

The supply-demand flow of food is crucial for accurately assessing ecosystem services and human well-being. However, quantifying the direction and intensity of dynamic ecosystem service (ES) flows is challenge. This study establishes a social-ecological network (SEN) framework to quantify the supply-demand flow of grain ES, which involves analyzing spatiotemporal patterns of supply and demand to distinguish between social and ecological aspects, identifying supply and demand nodes to define administrative boundaries, building a social-ecological network between supply and demand nodes, and measuring the intensity and direction of the supply-demand flow of grain ES. Taking the Pearl River Basin of China as an example, the supply-demand flow of grain ES was analyzed across three different scales during 2000–2019. The results indicated that there are significant spatial differences and mismatches in the supply-demand-flow of grain ES. The grain supply ranges from 29.18 Mt to 32.42 Mt, while grain demand ranges from 19.51 Mt to 25.28 Mt, respectively. The evolution of grain SEN reveals that most flow exhibits local characteristics, six prefecture-level city demand nodes were expected, and five prefecture-level city supply nodes with the highest flow intensity contributed a total flow intensity of 8.06–10.10 Mt. The supply nodes increased from 74.5% to 82.5% and demand nodes decreased from 25.5% to 17.5% during 2000–2019. The SEN approach can be effectively and visually used to quantify the supply-demand-flow of grain ES, support ecosystem management, and promote decision-making on food resource allocation.

Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks

The well-publicized disinformation campaigns surrounding recent elections, pandemic vaccination adoption, as well as supply-chain disruptions and shortages have made historical problems of disinformation more apparent. When disinformation targets transportation infrastructure, supply chains can be disrupted, resulting in commodities shortages such as food and fuel, consequently jeopardizing the health and safety of communities. This research proposes an integrated epidemiological-optimization model that quantifies the impacts of weaponized disinformation on infrastructure networks that transport multiple commodities. The model aims to minimize the overall weighted shortage of commodities caused by different disinformation spread rates. Results show that disinformation weaponized against transportation infrastructure, potentially targeting a particular geographical region or a particular commodity, can have wide-ranging impacts across different commodities. The proposed model is applied to the multi-commodity Swedish railway network carrying 14 different categories of commodities over 1360 supply and demand nodes.

Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data

BackgroundGravity models are often hard to apply in practice due to their data-hungry nature. Standard implementations of gravity models require that data on each variable is available for each supply node. Since these model types are often applied in a competitive context, data availability of specific variables is commonly limited to a subset of supply nodes.MethodsThis paper introduces a methodology that accommodates the use of variables for which data availability is incomplete, developed for a health care context, but more broadly applicable. The study uses simulated data to evaluate the performance of the proposed methodology in comparison with a conventional approach of dropping variables from the model.ResultsIt is shown that the proposed methodology is able to improve overall model accuracy compared to dropping variables from the model, and that model accuracy is considerably improved within the subset of supply nodes for which data is available, even when that availability is sparse.ConclusionThe proposed methodology is a viable approach to improve the performance of gravity models in a competitive health care context, where data availability is limited, and especially where a the supply nodes with complete data are most relevant for the practitioner.

Two-stage stochastic programming approach for gas allocation network under uncertainty

Pipelines enable enormous amounts of various products (fluids) to be transported from supply nodes to demand nodes. They have traditionally been recognized as the most efficient and secure method of transferring gases. Uncertainties in the parameters may develop in the actual world for a variety of reasons which reduces the efficiency of the gas allocation network (GAN). The amount of available gas cannot be forecasted exactly due to the uncertainty in gas supply and shared usage by other demands. Design of GAN is carried out in a two stage manner: installation and operation. During operation, there is always a chance of change in number of sources or demands than that of initial design phase. In this paper, to deal with such discrete uncertainties, a two-stage stochastic programming approach for GAN is developed. The first stage represents the installation and commissioning of supplying nodes in order to satisfy the demands, and the second stage represents the actual allocation network under different available supply stations scenarios. Illustrative examples are presented to demonstrate the proposed solution procedure and annualized investment for the examples are calculated. The calculated annualized investment is 12%, 28% and 38% less than the worst case solutions. Result explains the benefits of the model in reducing investment costs while incorporating such discrete uncertainties.

A VCO-Based ADC With Inherent Mixing Capability and Local Oscillator Suppression in 55-nm CMOS

This brief introduces a new circuit that can be used as a mixer and as a VCO-ADC simultaneously. The power supply nodes of the inverters in a ring oscillator are sorted in two sets, and connected to two different input signals. We prove mathematically that the oscillator frequency of such configuration reacts to the product of the two analog inputs. Therefore, the ring oscillator can be used as an ADC to directly digitize the low pass down converted component. A first system-level approach is made to minimize the non desired beat components by a proper number of taps in the ring oscillator. To confirm the concept of the Mixer VCO-ADC and its performance, a 55nm CMOS chip has been designed and fabricated. Measurements show a SNDR of 51.3 dB in a 100 kHz bandwidth in the down converted signal from a 1.001 MHz input signal mixed with a 1 MHz local oscillator.

A Novel Voltage/var Sensitivity Calculation Method to Partition the Distribution Network Containing Renewable Energy

Abstract: As the current calculation method of voltage/var sensitivity (VVS) could not reflect the temporal variations of power sources and load, the paper proposes a novel VVS calculation method to partition the distribution network containing renewable energy. Firstly, the defects of the existing VVS calculation methods are analyzed, and a novel VVS calculation method is proposed by adding reactive power output perturbation to the reactive power source. Secondly, the VVS matrix is fuzzified to obtain the membership matrix between each node. Based on the membership relationship between the reactive power source nodes, the nodes with a strong coupling relationship are merged to determine the initial number of partitioning. Then, the final partitioning result is determined according to the affiliation between each load node and each reactive power source. Finally, the partitioning result is evaluated based on the partitioning membership indexes. The proposed approach is tested on the IEEE 33-node distribution test system, and numerical simulations verify the high efficiency of partitioning. The simulation results show that the proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. Background: The voltage/var control of the power system is an important means to ensure the safe and economically stable operation of the system. Objective: The paper proposes a novel VVS calculation method to partition the distribution network containing renewable energy. Method: Firstly, the existing problems of electrical distance index are analyzed. A novel VVS matrix calculation method is proposed, which calculates the VVS of each reactive power source to the remaining nodes by adding voltage/var output perturbation to the reactive power source. Secondly, the VVS matrix is fuzzified to obtain the membership matrix between each node. Based on the membership relationship between the reactive power source nodes, the power supply nodes with strong coupling relationship are merged to determine the initial number of the partitioning. Then, the final partitioning result is determined according to the affiliation between each load node and each reactive power source. Finally, the partitioning result is evaluated based on the sensitivity index of membership degree. Results: The proposed approach is tested on the IEEE 33-node distribution test system and numerical simulations verify high efficiency of the partitioning. The simulation results show that the proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. Conclusion: The proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. This paper proposed a fuzzy clustering partitioning based on a novel calculation method of VVS, which is suitable for the problem of variable voltage /var running state of the system under high-proportion DG access.

The transportation problem with packing constraints

We address a new variant of the transportation problem, where a set of different commodities, available at several supply nodes with different prices, are distributed to demand nodes using a set of heterogeneous vehicle fleets available at each supply node. Since each commodity has a specific weight, there is a packing constraint due to the total capacity of each vehicle. The objective of this problem is to minimize the total supply cost, which includes the purchase cost of commodities and the fixed transportation cost based on the traveled distance. This novel variant of the transportation problem poses computational challenges due to its combinatorial structure. To tackle these challenges and solve large-scale instances to near-optimality in reasonable CPU times, we propose a variable neighborhood search algorithm. The proposed algorithm partitions the problem into smaller, more manageable problems to be solved. In order to initialize this methodology, we also propose a decomposition heuristic that prescribes initial feasible solutions in CPU times ranging from 0.6 s to 133.2 s on average with average optimality gaps ranging from 0.09% to 4.62%. Our proposed methodology provides solutions with 0.06% to 3.78% optimality gaps on average within average CPU times ranging from 0.2 s to 386.2 s, while the commercial solvers are unable to find integer solutions or to solve most of the instances to optimality within one CPU-hour time limit.

Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation

To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

Operational Identification of Resistances of Wires of 380 V Distribution Networks by Automated Accounting Systems

Four-wire distribution electrical networks of 0.4 kV equipped with automated systems of electrical energy accounting are considered. The problem of identifying the resistances of the wires of the distribution network is solved on the basis of the effective values of voltages and currents, as well as of their phase shift angles obtained by the accounting system in the power supply node of the network and from its subscribers for the selected observation intervals. A brief analysis of the known methods and technologies used in this area of research is carried out. The importance of the formulated problem for applied problems, such as control and diagnostics of electrical energy losses, as well as the technical condition of the network, is noted. A method (algorithm) is proposed that allows determining unknown, unequal complex resistances of inter-subscriber sections of the distribution network. In these resistances, the reactive components are considered equal within the inter-subscriber section; the active components differ due to the influence of unequal flowing currents and/or weather factors. At the same time, data from two different network operating modes are required, which are selected based on the analysis of the dynamics of changes of supply currents and/or voltages by the accounting system device connected to the power supply node. Considering that the active resistances of the wires must remain unchanged, the mode that is used for calculations is the one that is before the change in power consumption in the network and the next one immediately (about 0.1 s) after it. An example of a calculation that demonstrates the reliability of the proposed equations of the method that has been developed on a simulated distribution network is given. The research results are focused on the improvement of automated accounting systems and the implementation of their new functions that elevate the reliability of distribution networks, as well as allowing for the rapid identification of non-technical losses of electrical energy.

Provision of optimal dispatching scenarios for regional power systems in the face of uncontrollable power shortages

As of 2023, substantiation of the criteria for optimal load balancing of territorial energy islands in the face of uncontrolled power shortages due to unpredictable failure (destruction as a result of military bombardment) of the upper (trunk) level energy infrastructure is relevant. The research aims to substantiate approaches to ensuring the controlled operation of regional power systems under conditions of power shortages. The study applied the theory and methods of mathematical analysis of complex multicomponent systems in the form of mixed Boolean linear programming. A Mathematical Programming Language and modelling methods of the NEOS server based on the Gurobi solver were used to describe the modes of operation of power grids. A proportional power distribution method between load nodes of the post-emergency network configuration was used to determine the direction of electricity transit with the least losses. An algorithm and a program for solving the problem of addressing flows and power losses in multi-node regional power systems are proposed. The optimisation problem is formulated in the form of a mixed Boolean linear programming model with the criterion of minimising the power momentum, considering balance constraints and upper limits on the power between supply and load nodes. The developed transport matrix made it possible to find the optimal power distribution for emergency and post-emergency modes according to the criterion of minimum network losses. Algorithms and scenarios for the response of dispatching services were formed, considering the capacity of power grids and determining temporary power supply schemes, the configuration of which will ensure the “survivability” of energy islands. The levels of efficiency of the Boolean linear programming model associated with the connectivity of the cycles of the transport problem and the order of traversal of its vertices for test examples depending on the constraints imposed on the components of the network structure are established. The process of tracking electricity flows will allow for establishing routes connecting specific electricity supply nodes with load nodes and determining their shares in covering the energy island’s demand under conditions of power shortage

Development of a MATLAB-GAMS Framework for Solving the Problem Regarding the Optimal Location and Sizing of PV Sources in Distribution Networks

This paper addresses the planning problem regarding the location and sizing of PV generators in distribution networks with a radial topology. This problem is mathematically modeled using a mixed integer nonlinear programming (MINLP) model, which seeks to reduce the total annual operating costs of the system for a planning horizon of 20 years. The objective function used in this paper comprises three elements: (i) the energy purchase costs at the substation node (i.e., the main supply node), (ii) the investment costs for the integration of PV generators, and (iii) the costs associated with the operation and maintenance of these devices. To solve this problem, the interconnection of MATLAB and GAMS software is proposed, while using a master–slave methodology, with which a high-quality solution to this problem is achieved. In the master stage, the MATLAB software is used as a tool to program a discrete version of the sine–cosine algorithm (DSCA), which determines the locations where the PV generators are to be installed. In the slave stage, using one of the solvers of the GAMS software (BONMIN) with the known locations of the PV generators, the MINLP model representing the problem to be studied is solved in order to find the value of the objective function and the nominal power of the PV generators. The numerical results achieved in the IEEE 33- and 69-node systems are compared with the mixed-integer conic programming model solution reported in the specialized literature, thus demonstrating the efficiency and robustness of the proposed optimization methodology.