Network Constraints Research Articles

State inference for low-observable distribution system based on graph convolutional network

Distribution network state inference refers to the process of calculating the state variables of each node by using measurement data and network models in the operation of the distribution system. However, the uneven measurement layout and insufficient measurement accuracy in the distribution network have brought great challenges to the state inference of the distribution network. This paper proposes a low-observable distribution network state inference method based on a graph convolution network (GCN), which uses sparse measurement data to infer missing measurement information. Firstly, the observability of the distribution network is analyzed by the numerical probability analysis method. Secondly, the GCN is employed to extract feature information from measurement data and integrate these features. The state inference model of the distribution network based on the GCN is established. Subsequently, power flow constraints of the distribution network are incorporated into the GCN training process to enhance the precision of the generated data. Ultimately, the efficacy of the proposed method is validated using the IEEE 33-node distribution system.

Scalable Cloud Architectures for Deploying AI Applications

The integration of Artificial Intelligence (AI) into cloud computing represents a pivotal shift in the technological landscape, offering unprecedented opportunities for innovation across industries. Deploying AI applications at scale on the cloud poses unique challenges, necessitating the development of scalable cloud architectures tailored to meet the computational demands and data processing needs inherent to AI. This article explores the foundational aspects of cloud computing architectures, emphasizing the principles of scalability essential for AI applications. It delves into the core challenges faced when deploying AI on the cloud, such as computational requirements, data management, network constraints, and security concerns. Further, it presents various architectural models that facilitate scalability, including containerization, serverless computing, and cloud-native AI services, drawing from realworld case studies to illustrate effective strategies and best practices. Additionally, the article examines performance optimization techniques, security considerations, and the future directions of cloud-based AI deployments, highlighting the role of emerging technologies such as quantum computing and edge AI. By providing a comprehensive overview of scalable cloud architectures for AI applications, this article aims to guide researchers, practitioners, and organizations in leveraging cloud computing to its full potential, thereby enabling more efficient, secure, and scalable AI solutions.

Enhancing Quality of Service and Energy Efficiency in Wireless Sensor Networks through TDMA-Based Topology and Cross-Layer Approaches

It is necessary to continuously monitor the environment with the sensors in delay-sensitive wireless sensor network applications. The continuous monitoring operation results in higher energy consumption by the sensor nodes; wireless sensor networks are also subject to numerous difficulties, including latency, packet loss, and throughput. These are the most important constraints in wireless sensor networks (WSNs) and need to improve for better quality communication. In this research, quality of service is related to numerous systems that have very sensitive parameters such as end-to-end delay, packet loss, throughput, energy consumption, latency, and average delay. Therefore, it is necessary for the sensors to transmit reliable data within a reasonable timeframe. To find out better communication results, we have used the TDMA (Time Division Multiple Access) approaches in our paper to improve packet delivery ratio (PDR), latency, and throughput. The NS2 simulator is used to test communication between nodes. Latency, the packet delivery ratio (PDR) of transferring data packets, and throughput are measured. The TDMA approach is used in our proposed topology, and we can see a specific area where nodes are connected to communicate with one another. Each node is communicating with the other node using two different paths. Each node's bandwidth, latency, and PDR will be different. Hence, in one path, a TDMA approach is implemented, which results in improved node performance in terms of latency, packet delivery ratio, and bandwidth.

Improvement of Commercial Vehicle Seat Suspension Employing a Mechatronic Inerter Element

To further improve the ride comfort of commercial vehicles, a seat ISD (Inerter–Spring–Damper) suspension utilizing a mechatronic inerter is proposed in this paper. Firstly, a five-DOF (degree-of-freedom) commercial vehicle seat ISD model was built. Then, the positive real network constraint conditions of a biquadratic impedance transfer function were determined, and the meta-heuristic intelligent optimization algorithm was used to solve the parameters. According to the solution, the impedance transfer function was obtained and the specific network structure was realized by network synthesis. Lastly, this study compares the vibration isolation performance of the mechatronic ISD suspension of the vehicle seat with that of a passive suspension. In comparison to passive seat suspension, the seat mechatronic ISD suspension reduces seat vibration transmissibility by 16.33% and vertical acceleration by 16.78%. Results indicate that the new suspension system can be an effective improvement in ride comfort.

Perilaku Pencarian Informasi dengan Menggunakan Portal Jurnal Elektronik “ScienceDirect” dalam Pemenuhan Kebutuhan Informasi Mahasiswa

This research discusses "Information Search Behavior Using the Electronic Journal Portal "ScienceDirect" in Fulfilling Student Information Needs". The subject matter of this research is the information search behavior of students of the Library Science Study Program at the State Islamic University of North Sumatra. This type of research uses descriptive methods, with a qualitative research approach. This approach aims to identify information search behavior using ScienceDirect Information search behavior using the ScienceDirect electronic journal portal has conducted searches using the stages put forward by Ellis. The results showed that the purpose of information searchers using ScienceDirect was to find references in completing coursework and looking for journals only as reading material. The information sought is articles related to courses majoring in library science and looking for scientific papers on losing weight. In Ellis' theory, there are 8 stages that are passed in the information search process, namely, starting, chaining, browsing, differenting, monitoring, extracting, verifying, and ending. From the analysis, it can be concluded that all informants go through these 8 stages to get the information they need. Some of the differences between using ScienceDirect and using other portals: (a) Availability of articles (sometimes in ScienceDirect not in other portals, and vice versa) (b) Articles obtained are in English, other portals use Indonesian (c) Provides scientific papers from international authorized authors Some obstacles in searching for information using ScienceDirect electronic journals: (a) The articles searched are not very complete (b) Articles in English (c) Network constraints (d) Many paid articles (e) Having to register Many articles are paid (e) Must register for an account (f) Search settings are not easy.

Network constraint and online group innovation: a dual perspective of inter-community group network and intra-community group network

PurposeThis study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.Design/methodology/approachThe authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.FindingsThe results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.Originality/valueThe findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability

The virtual power plant (VPP) provides an effective way for the coordinated and optimized operation of distributed energy resources (DERs). To solve the aggregation problem of a VPP containing scattered layouts and heterogeneous performance DERs, this study proposes a dynamic aggregation strategy to improve the flexible regulation ability of the VPP. A VPP aggregation model considering network constraints and temporal coupling constraints of DERs is constructed, while some VPP performance parameters are proposed to characterize and quantify the regulation ability. An uncertainty set considering time-series coupling properties of variables is constructed, and an aggregation method under uncertainty scenarios is proposed based on a two-stage robust optimization model. In addition, a dynamic aggregation strategy is proposed for the application of VPPs in electricity markets. Finally, case studies demonstrates that the proposed method provides a more extensive feasible region compared to other methods, and the deviation power remains within a reasonable range. The dynamic aggregation strategy facilitates the synergistic and correlative operation of DERs, exploits the regulation ability of the VPP in frequency regulation and spinning reserve, and improves the feasibility of practical applications. Simultaneously, the income of the VPP increased by 29.5 %.

Siting and operating incentives in electrical networks: A study of mispricing in zonal markets

The incentives electricity generators face in investment and output decisions hinge on market design. Under some zonal market designs, where profit-maximizing participants face a uniform regional price, achieving lowest-cost system-wide production can be impossible. Further, zonal designs can incentivize siting of intermittent renewables in inefficient locations behind network constraints, of concern for jurisdictions undergoing a clean energy transition. We develop measures of mispricing that compare the zonal prices generators receive to locational marginal prices that value congestion externalities from generator output. We apply these measures to show wind and solar generators are increasingly siting in constrained areas of the Australian network, and highlight sources of potential efficiency gains from adopting a nodal market design with locational marginal pricing.

Smoothness and monotonicity constraints for neural networks using ICEnet

Abstract Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the generalized linear models (GLMs) currently used in industry. Although constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation model interpretability technique.

Dampak Dokumentasi Elektronik terhadap Implementasi, Manfaat dan Hambatan pada Pelayanan Home Care

This study aims to determine the role of digitization in the documentation of nursing care for community nurses in home care services. Involving aspects of implementation, benefits and barriers in using electronic nursing documentation. The method used was using electronic databases such as Ebsco, Scopus, Proquest, and PubMed published between 2020-2023. Based on the initial search, there were 8 relevant articles out of 320 articles found. The results showed that the implementation of electronic nursing care documentation has a positive impact on patients, nurses and health services. However, several obstacles were found, namely the skills and knowledge of nurses on electronic documentation are still lacking, so training is needed. Internet network constraints are also very influential, especially in rural areas. In conclusion, there is a gap between the need for internet and internet distribution which is a challenge for the government in developing digitalization in the health sector and improving nurses' skills in electronic documentation. Keywords: Electronic Documentation, Barriers, Benefits

From ionic clusters dynamics to network constraints in ionic polymer solutions.

Physical networks formed by ionizable polymers with ionic clusters as crosslinks are controlled by coupled dynamics that transcend from ionic clusters through chain motion to macroscopic response. Here, the coupled dynamics, across length scales, from the ionic clusters to the networks in toluene swollen polystyrene sulfonate networks, were directly correlated, as the electrostatic environment of the physical crosslinks was altered. The multiscale insight is attained by coupling neutron spin echo measurements with molecular dynamics simulations, carried out to times typical of relaxation of polymers in solutions. The experimental dynamic structure factor is in outstanding agreement with the one calculated from computer simulations, as the networks are perturbed by elevating the temperature and changing the electrostatic environment. In toluene, the long-lived clusters remain stable over hundreds of ns across a broad temperature range, while the polymer network remains dynamic. Though the size of the clusters changes as the dielectric constant of the solvent is modified through the addition of ethanol, they remain stable but morph, enhancing the polymer chain dynamics.

Joint flexibility-risk managed distributed energy trading considering network constraints and uncertainty

This paper addresses the operational challenges in distribution systems caused by the integration of fluctuating renewable energy sources and rising peak loads from widespread electrification. We propose a new distributed energy coordination framework that facilitates seamless interoperation between peer-to-peer (P2P) energy trading and the operation of distributed flexibility resources. This framework ensures network constraints are respected while promoting Energy Trading Consistency (ETC). Our approach utilizes a two-stage dynamic energy and flexibility sharing mechanism. It achieves energy efficiency through demand-management and P2P energy trading in the day-ahead (DA) stage, followed by adjustments of flexible resources and flexibility sharing through a receding-horizon feedback mechanism inspired by model predictive control (MPC) in the real-time (RT) stage. High-risk scenarios and computational overhead are managed by employing conditional value-at-risk (CVaR) measures, as well as approximation of constraints through dual robust reformulation and linear decision rules. To uphold the autonomy and privacy of prosumers, we propose distributed algorithms based on the fast consensus alternating direction method of multipliers (ADMM). Numerical results demonstrate that our framework achieves a 20% reduction in social cost while providing a coherent and interactive P2P energy trading environment, facilitating reliable network operation under high penetration of renewable energy sources without third-party intervention.

Transportable energy storage assisted post-disaster restoration of distribution networks with renewable generations

More frequent extreme weather events due to climate change impose significant challenges on the post-disaster restoration of distribution systems with significant penetration of renewable generations. To address this challenge, this paper investigates a restoration scheme for distribution networks integrated with renewable generations, and transportable energy storage systems moving along a transportation network, such as railway or road network, are used to support the network restoration after the fault event. To achieve this, for given fault conditions, topological reconfiguration is first facilitated via the soft open point (SOP) technology, and local renewable generators and loads are rapidly regrouped to form microgrids for post-disaster restoration. Then, assisted with transportable energy storage, the resources within the microgrids can be dispatched more efficiently, facilitating a more efficient post-disaster recovery process. To enable a cost-effective restoration of the distribution network, an economic model is formulated with the aim of minimizing the economic losses during the post-disaster restoration process. Case studies considering a significant number of failure scenarios show that the proposed transportable energy storage-assisted restoration scheme can effectively minimize costs during the post-disaster period considering various load types across different areas. This scheme not only achieves cost-effective post-disaster restoration but also ensures compliance with the constraints of the electrical networks, achieving up to 93.3% recovery cost reduction and 74.25% increase in critical load restoration.

A coordinated operation method for networked hydrogen-power-transportation system

Hydrogen fuel cell vehicles have been promoted as a complement to electric vehicles (EVs) to facilitate the decarbonization of transportation networks (TNs). The coordinated operation of a networked hydrogen-power-transportation system with distributed hydrogen supplies is proposed in this research. To maximize the synergistic effect, TN's couplings in hydrogen transport delays and refueling/charging demand are further integrated into the conventional hydrogen-power system. The objective is to maximize the total profits, subject to both the coupling and network constraints. To present the storage effect of hydrogen tube trailers with delays, an extended discrete user equilibrium (UE) is developed and the traditional UE is adopted to formulate refueling-charging demand couplings. Considering the hydrogen supply process, it especially involves the constraints of hydrogen production, storage, utilization, and dispensation. A data-driven robust chance-constrained programming is provided to account for multiple uncertainties from energy/traffic demand and renewable power. Simulation results of the 48-node system show that the proposed model improves the total profits.

Merchant EV charging station expansion planning

The significant expansion of Electric Vehicles (EV) creates an uncertain load demand that may pose many challenges to conventional planning methods, especially at the distribution network level. The development of EV technology requires charging infrastructure, which must be considered in the Distribution System Expansion Planning (DSEP). Due to the presence of EV and the load they impose on the distribution system, the planning of EV charging stations is done simultaneously with the DSEP so that the system operator can properly consider the technical constraints of the network and operational constraints. This paper presents a bi-level model for EV charging station expansion planning. The main purpose of the first level problem is expansion planning of charging stations and distribution system. But the charging stations are mostly built by a merchant investor, and they do so when it is profitable and has a reasonable risk. Therefore, the merchant investor should be encouraged to build a charging station in the planned areas. For this reason, in the second level problem, the profitability of the charging station and its risk are examined. To solve this problem, genetic algorithm (GA) has been used. Finally, to test the proposed model, the simulation was performed on a modified 69-bus IEEE network.

Cellular network-based IIoT architecture for time-critical control tasks of building automation

Industrial Internet of Things (IIoT) adopting emerging cellular networks attracted great attention in various industrial fields, including the building sector. However, the capability of IIoT and the performance of cellular networks when applie for building automation (BA), especially for time-critical control tasks, are not effectively evaluated yet. The potential impacts of network constraints on the control performance also need further investigation for the alternative architecture. These fundamental questions have not been addressed sufficiently yet. This study therefore proposes a cellular network-based IIoT architecture as a newly-proposed and recommendable solution for building automation systems. The architecture consists of smart field-level devices embedded with cellular network-enabled IoT controllers and a configured cellular network. Performance of the latest cellular networks, including 4th generation (4G) and 5th generation (5G) networks, are experimentally tested under different conditions. A typical building time-critical control task is selected to assess the capability of proposed architecture. The results show that the proposed IIoT architecture adopting 4G and 5G networks has good capability for the time-critical control tasks.

Feasible operation region estimation of virtual power plant considering heterogeneity and uncertainty of distributed energy resources

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation.

MPC-based framework incorporating pre-disaster and post-disaster actions and transportation network constraints for weather-resilient power distribution networks

Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two-stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre-disaster operational planning strategy is defined to prepare the grid for a high-risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed-integer quadratic programming model. In the second stage, a post-disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real-world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first-stage operational costs and second-stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services.

Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method.

A New Model for Matching Asks and Bids in P2P Energy Trading: Game Theory Method

Peer-to-Peer (P2P) energy trading is one of the emerging approaches for managing energy within microgrids. Various methods and mechanisms have been proposed for market settlement in P2P energy trading. One of the challenges in these approaches is the underutilization of the maximum capacity of the P2P energy market. In this article, to maximize the utilization of the P2P market capacity and address the buying and selling requests, a combination of auction-based and bilateral negotiation approaches has been utilized. One of the advantages of double auction-based approaches is the ability to maximize social welfare due to the presence of an auctioneer in their structure. On the other hand, one of the challenges of these approaches is that some buyers and sellers may exit the market clearing process based on their own bids and asks. As a result, they would need to find alternative ways to sell and buy their energy. In the proposed approach, buyers and sellers are divided into two groups. The first group consists of participants who have won in the auction process. In this scenario, buyers engage in a Stackelberg game with the P2P energy trading platform, which acts as the auctioneer. In this game, the auctioneer plays the role of the leader, and the buyers play the role of followers. Each of them strives to maximize their social welfare function. After the completion of the market settlement process for the first group, the buyers and sellers of the second group attempt to match their contracts with each other through bilateral negotiations. Another challenge in P2P energy trading is the lack of simultaneous consideration of both economic (market settlement) and technical aspects (network constraints). In the proposed mechanism, constraints and network topology are also considered in the transactions process to minimize the possibility of overload and line congestion. The proposed mechanism has been implemented on the IEEE 37-feeder test system, and the results show that the proposed mechanism performs significantly better than other existing methods.