Power Resource Management Research Articles

Series and shunt FACTS controllers based optimal reactive power dispatch

Optimal reactive power dispatch involves the determination and management of reactive power resources in a power system to maintain voltage stability, improve power transfer capability, and minimize system losses. Reactive power is essential for maintaining voltage levels within acceptable limits and ensuring the reliable operation of electrical networks. The whale optimization algorithm (WOA) has been proposed to obtain the optimal location of flexible alternating current transmission system (FACTS) components. The efficacy of WOA is tested using conventional IEEE 14 and 30 bus test systems. Static var compensator (SVC) is used as shunt and the thyristor-controlled series capacitor (TCSC) as a series FACTS controller. The analysis is carried out for both the systems with and without FACTS controllers. Optimization techniques are applied to select the optimal control parameters. The suggested strategy is compared to other contemporary techniques such as particle swarm optimization (PSO) and grey wolf optimization (GWO). At various loading situations, the WOA-based technique outperforms other two techniques.

Taxonomic Exploration of Healthcare IoT: Challenges, Solutions, and Future Frontiers

An Internet of things (IoT) ecosystem is a fast-developing network in which users can connect a heterogeneity of physical and virtual devices, including customized healthcare areas. As medical resources are scarce, populations are aging with chronic diseases and require remote monitoring, medical expenses are rising, and telemedicine is being demanded in developing nations, the IoT is an attractive topic in healthcare. Through the IoT, people can enjoy better health and diminish pressure on sanitary systems. In this study, previously published studies in Healthcare IoT (HIoT) systems are detailed, analyzed, and taxonomically classified. By categorizing the articles according to the types of HIoT systems, we dispense a detailed taxonomical study. In addition, different evaluation methodologies, tools, and metrics are discussed, along with their advantages and disadvantages. The studies indicate that power management, trust, privacy, fog computing, and resource management are among the open issues. The future of the Internet includes tactile networks, social networks, big data analytics, software-defined networking, network function virtualization, the Internet of nano things (IoNT), and blockchain. It would be beneficial to study and research HioT systems further in terms of interoperability, the implementation of real-world test beds, scalability, and mobility.

Trade unions and institutional power resources in the United Kingdom

PurposeThe paper examines the approach of United Kingdom (UK) Trade Unions to the use of institutional power resources (IPR) in the second half of the twentieth century.Design/methodology/approachUsing secondary material, it examines the unions' approach to IPR in three cases; collective bargaining; worker representation and trade union structure.FindingsThe paper concludes that unions did not appreciate the importance of, and lacked a strategic approach to, IPR. Although employer and government action were largely responsible for the decline of industrial relations institutions, the failure of the unions to engage with IPR contributed to this process. It explains the failure of the unions to engage with IPR by reference to their lack of strategic capabilities and skills in relation to power resources (PRs) in general and IPR in particular.Research limitations/implicationsIt would have been interesting to collect primary data via interviews with union actors from the period examined to test the interpretation of secondary data contained in the article.Practical implicationsThe paper has identified the kind of strategic decision-making which is necessary for unions to engage effectively with IPR. It is has also indicated the key skills which unions need to develop to be able to manage their engagement with IPR.Social implicationsThe paper has implications for the role of trade unions in society, showing the need for them to develop narratives to convince society of the importance of their role and action and to develop the skills which enable them to connect with other social groups, e.g. intermediation, engagement with coalitional resources.Originality/valueAnalysis of the decline of industrial relations institutions in this period has emphasized the role of employers and the state. This paper contributes to a more balanced perspective on this decline by drawing attention to the lack of a union strategy towards IPR and the importance of the detail of the management and employment of power resources.

Preface for “renewable power generation, energy storage, and resource management challenges and solutions”

Preface for “renewable power generation, energy storage, and resource management challenges and solutions”

How to balance the industrial customers' resources requirements while maintaining energy efficiency?

One of the most important challenges facing the optimal management of power resources for industrial customers is considering the latest available technologies for this purpose. In this regard, this article investigates the most important technologies and methods available in this field and tries to maintain the energy efficiency at a suitable level by presenting a knowledge-oriented approach. One of the most important technologies used in this field is demand response, which provides an opportunity for consumers to provide an appropriate response to energy at various price rates by reducing or changing electricity consumption in peak load. In addition, the emergence of different renewable energy sources such as solar and wind in recent years has created a suitable opportunity for the participation of the majority of industrial and household consumers and as a result has increased social awareness in this field. The above items are investigated and discussed one by one in this paper and the impact of each of them on meeting the needs of industrial customers is assessed. In the end, it is tried to present a model to show how to optimally use these resources in scheduling from the social, economic and technical point of view.

Digital Management and Optimization of Tourism Information Resources Based on Machine Learning

With the gradual growth of economy, tourism has become a pillar industry in many countries and plays an important role in promoting national development. The individualization and diversification of tourism resources must be supported by a powerful information resource management system. However, the traditional tourism information resource management system has some problems, such as scattered sources of tourism information, low interactivity, and slow update of information resources. Tourists cannot get detailed information of scenic spots and make detailed plans for tourism which hinder the further development of the tourism industry. In order to solve these problems and promote the development of the tourism industry, this paper carried out digital management of tourism information based on machine learning and digital management of information resources of different tourist attractions and surveyed and tested the number of tourists, expenditure of scenic spots, income of scenic spots, and satisfaction of tourists. The total result showed that the digital management of tourism information resources in scenic spots can increase the passenger flow, increase the income of scenic spots, reduce the expenditure of scenic spots by 6.7%, and increase the satisfaction of tourists by 4.1%. The digital management of tourism information resources based on machine learning can optimize the tourism industry and promote its development.

A modified graywolf‐basedchameleon swarm algorithm for minimizing energy consumption and enabling secure communication in wireless sensor network

SummaryThe efficiency and security of WSNs are improved by designing multiple lightweight cryptographic techniques and a 6LoWPAN protocol, depending on resource utilization and a reasonable amount of energy. This is still caused by constraints such as power resource management, authentication, key management, and flexibility protocols. To address these challenges, a novel approach called the modified gray wolf‐based chameleon swarm (MGW‐CS) algorithm is presented to select the cluster heads from WSN nodes. The MGW‐CS algorithm is an amalgamation of both improved gray wolf optimizer and chameleon swarm algorithm. During cluster head selection, the QoS parameters such as network lifetime, energy efficiency, and throughput and packet delivery ratio are improved and optimized. By presenting a lightweight and flexible cryptographic model to manage encryption complexities, the encryption parameters are automatically selected based on the currently available resources of each sensor node for data encryption. Among various WSN nodes, data exchanging and secure communication are established with the development of novel authentication and a lightweight key management model. The proposed model is implemented using the NS‐2 software. The proposed methodology offers a packet delivery rate of 98.6% for a total of 500 nodes when compared to the different state‐of‐art techniques.

Muformer: A long sequence time-series forecasting model based on modified multi-head attention

Long sequence time-series forecasting (LSTF) problems are widespread in the real world, such as weather forecasting, stock market forecasting, and power resource management. LSTF demands the model to have a high prediction accuracy. Recent studies have shown that transformers have the potential to improve predictive accuracy. However, we found that Transformer still has severe problems preventing it from directly applying to LSTF, such as redundant input information, which makes it difficult to provide accurate predictions. In order to solve this problem, this paper proposes an efficient transformer-based predictive model called Muformer. The model includes (1) an input multiple perceptual domain (MPD) processing mechanism, which can process a single input data into N outputs of different perceptual domains, thereby playing a role in feature enhancement; (2) a multi-granularity attention head mechanism that can cooperate with the MPD mechanism: the N outputs of MPD are input into different attention heads so that the head information can be fully utilized to reduce the generation of redundant information; and (3) an attention head pruning mechanism, which prunes similar redundant information as that handled by multi-head attention, thereby reducing redundant head information and enhancing model expression. Extensive experimental results obtained on five large-scale datasets show that our approach significantly outperforms existing state-of-the-art methods.

Linear pricing game based power control with resource allocation and interference management in device‐to‐device communication for IoT applications

AbstractDevice‐to‐Device (D2D) communication is one of the leading technologies which works under the aegis of fifth generation (5G) communication for the Internet of Things in Healthcare. In D2D communication, power control with resource allocation is the most challenging area for the research to make them suitable for IoT devices. In the present work, power control with resource allocation is done by using the Linear pricing game power allocation method. First, a mathematical min‐function based problem is proposed whilst taking the interference, data rate and minimum power requirement as a constraint to ensure the resource and power allocation. In the next phase, a linear pricing game (LPG) method is utilized to propose the LPG power control with resource allocation (L‐PaRAG). Mathematical analysis and theorem ensure the minimum cost function with global stability for an optimal solution of the proposed problem. Numerical analysis and results show that the proposed algorithm gives the best results for power control and resource management whilst managing the interference to maintain the quality of services (QoS) in D2D communication. Optimized power can be utilized for the communication between the base station and D2D users, especially, during disaster management.

Power Optimization Model for Energy Sustainability in 6G Wireless Networks

Internet-of-Things (IoT) networks are witnessing a rapid proliferation of connected devices and mobile terminals each day. The wireless information flow between these massive battery-powered devices has a huge energy burden and will lead to an energy crisis in the near future; thus, there is an urgent search for sustainable energy networks. To offer a sustainable energy solution in order to meet the energy demands of these massive IoT networks, this paper presents a dynamic practical model that enables the efficient management of power resources. Two user-scheduling algorithms, namely, minimum distance scheduling (MDS) and maximum channel gain scheduling (MCS), are proposed; when these algorithms were used alongside a power optimization, they led to improved network efficiency. Further, the network’s performance was measured with parametric variations in the number of access points (APs); the deployment of APs and AP configuration is carried out for different precoding schemes. The impact of spatial correlation and the access to perfect channel state information (CSI) on the spectral efficiency of the system was also evaluated. In the end, the study compares the performance of different power-allocation methods and suggests that the power allocated to a particular user node by an AP can be controlled using the proposed algorithms. It is observed that, as compared to the MDS algorithm, the MCS algorithm results in better spectral efficiency for all the users with fractional power allocation. In addition, each AP assigns a maximum power of 141.7 mW to a user with strong channel conditions with the AP, and a minimum power of 3.1882 mW to the user with the worst channel conditions using centralized PMMSE precoding.

A distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control

Smart grid integrates smart meters and modern communication technologies with the traditional electricity grid. It not only improves efficiency in power resource management, but also enhances reliability of the grid. However, the power consumption data may reveal users’ privacy, e.g. their living habits, and its authenticity is also important for accurate billing. Although a number of privacy preserving data aggregation schemes for smart grid have been proposed in the literature, most of them still suffer some limitations. For example, a single party is normally required to decrypt the aggregated data and this party could be a single point of failure in the system. Moreover, once the aggregated data is decrypted, no access control is imposed and it is available to the public. In order to address the above problems, we introduce a distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control. In the proposed scheme, homomorphic encryption is employed to ensure user privacy, digital signature is used to ensure data authenticity, distributed proxy re-encryption is used to achieve fine-grained access control of the aggregated data, and zero-knowledge proof is used to ensure that the proxy servers perform their tasks properly. Security analyses prove that the proposed scheme achieves the desirable security properties. In particular, it does not suffer single point of failure and the aggregated data can only be accessed by the designated recipient. Efficiency analyses also demonstrate that it is practical for real-world applications.

Learning to listen: Downstream effects of listening training on employees' relatedness, burnout, and turnover intentions

AbstractThe present work focuses on listening training as an example of a relational human resource practice that can improve human resource outcomes: Relatedness to colleagues, burnout, and turnover intentions. In two quasi‐field experiments, employees were assigned to either a group listening training or a control condition. Both immediately after training and after 3 weeks later, receiving listening training was shown to be linked to higher feelings of relatedness with colleagues, lower burnout, and lower turnover intentions. These findings suggest that listening training can be harnessed as a powerful human resource management tool to cultivate stronger relationships at work. The implications for Relational Coordination Theory, High‐Quality Connections Theory, and Self‐Determination Theory are discussed.

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A cross-layer resource management dynamic source routing (CLRM-DSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-to-end delay, and lower routing overhead for the multi-hop swarm UAV communication network.

Power and Radio Resource Management in Femtocell Networks for Interference Mitigation.

The growth of mobile traffic volume has been exploded because of the rapid improvement of mobile devices and their applications. Heterogeneous networks (HetNets) can be an attractive solution in order to adopt the exponential growth of wireless data. Femtocell networks are accommodated within the concept of HetNets. The implementation of femtocell networks has been considered as an innovative approach that can improve the network’s capacity. However, dense implementation and installation of femtocells would introduce interference, which reduces the network’s performance. Interference occurs when two adjacent femtocells are operated with the same radio resources. In this work, a scheme, which comprises two stages, is proposed. The first step is to distribute radio resources among femtocells, where each femtocell can identify the source of the interference. A constructed table is generated in order to measure the level of interference for each femtocell. Accordingly, the level of interference for each sub-channel can be recognized by all femtocells. The second stage includes a mechanism that helps femtocell base stations adjust their transmission power autonomously to alleviate the interference. It enforces a cost function, which should be realized by each femtocell. The cost function is calculated based on the production of undesirable interference impact, which is introduced by each femtocell. Hence, the transmission power is adjusted autonomously, where undesirable interference can be monitored and alleviated. The proposed scheme is evaluated through a MATLAB simulation and compared with other approaches. The simulation results show an improvement in the network’s capacity. Furthermore, the unfavorable impact of the interference can be managed and alleviated.

Evaluation of Performance of Cloud of Things (CoT) for Transferring Multimedia and Bulk-set Data

As the number of context-aware and unique data extracting and computing has grown leaps and bounds, paving way to their application in many platforms, Internet of Things (IoT) has gained a lot of importance in recent years. Using IoT, it is possible to connect any number of objects and in any context, giving rise to a diverse range of services. Hence power management, data storage, service discovery, service management, service creation and resource management need a more sophisticated mechanism and a better infrastructure. However, a single IoT that is power-constrained will not be able to tackle all the data that is generated. Hence cloud computing plays a crucial role by integrating with the IoT in such a way that the future internet and envisioned IoT is achievable. There are a number of challenges involved of which data trimming is common challenge. Due to unnecessary communication, the data this sent to the cloud may be interrupted. Hence to prevent this, data is initially preprocessed following which it is trimmed and then transferred to the cloud. This aspect of data processing can be carried out through Fog Computing or Smart Network that serves as a smart gateway. In this paper, we have introduced a novel concept Fog computing as the smart gateway. The results have been analysed and tested with respect to bulk-data synchronizstion Delay, bulk-data upload delay, jitter, synchronization delay and upload delay.

Trade-off between spectral efficiency and normalized energy in Ad-hoc wireless networks

With the ever-increasing deployment of wireless communication technologies, effective management of scarce power and radio spectrum resources is of primary concern. The available bandwidth spectrum should be utilized in the most efficient and controlled way for practical economic growth of industries in conformity with the evolving demand of energy for powering wireless devices. In the proposed optimization scheme, energy management and limited spectrum sharing are distributively implemented for handling quality of service provisioning and potential resource allocation across multiple nodes with inherent operational capability deficiencies. We employ cross-layer information exchange and convex optimization techniques to simultaneously achieve more efficient radio spectrum usage and optimal energy consumption in ad-hoc wireless networks with distributed scenarios. We consider the time-invariant additive white Gaussian noise channel and the time-varying Rayleigh fading channel to study the trade-off between the two network design objectives of achieving improved spectrum efficiency and minimizing the energy overhead in data routing paradigm. The original problem is transformed into an equivalent convex optimization problem through logarithmic processing to obtain the approximate global optimal solution. Moreover, the robustness and efficiency of the proposed framework is evaluated in large-scale set ups to provide the scalability analysis on both the employed performance objectives. The developed optimization scheme is compared with the machine learning models previously proposed in literature by employing the computational and/or time complexity metrics. Finally, the effectiveness of the proposed optimization model is substantiated through the simulation comparison results with the existing schemes in terms of various key performance parameters such as throughput, energy efficiency, and average bit errors.

An automated lightweight encryption scheme for secure and energy-efficient communication in wireless sensor networks

In wireless sensor networks (WSNs), security, efficiency, and energy consumption are still major challenges due to its open, large-scale, and resource-constrained nature. The 6LoWPAN protocol and many other lightweight cryptographic methods have been developed to improve the security and efficiency of WSNs with a reasonable amount of energy and resource utilization. However, they still suffer from limitations in terms of flexibility, key management, authentication, and the power resource management processes. This paper addresses these issues by designing FlexCrypt, an automated lightweight cryptographic scheme for WSNs. In the FlexCrypt scheme, a new dynamic clustering technique that supports mobility among the sensor nodes is developed. Moreover, the proposed scheme introduces a flexible lightweight cryptographic method to control the complexity of the encryption by automating the selection of encryption parameters based on the presently available resources of each sensor node to encrypt the data. In addition, a new lightweight key management and authentication method is designed to establish secure communication and the exchange of data and keys among the various WSN nodes. The proposed FlexCrypt scheme was evaluated using the Cooja simulator with Contiki OS. The results prove that the proposed scheme provides a significant improvement in terms of delay and encryption time in addition to power consumption and network lifetime compared to other ciphers that use fixed encryption parameters. It can effectively extend the network lifetime by 86%, 94% and 90% compared to the FlexenTech, AES and TEA ciphers, respectively. The security analysis demonstrates that the FlexCrypt scheme can resist various attacks such as brute-force, eavesdropping, man-in-the-middle and replay attacks.

Wireless Resource Management and Resilience Optimization of the M2M‐Oriented Mobile Communication System

Machine‐to‐machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low‐energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.

Trust and perception: key challenges facing geoscientists in practice and in Irish society

Society is underpinned by geoscience, from power generation, construction, communication, resource management, water supply and security, to responses to climate change impacts. Communicating the complexities and uncertainties that are familiar concepts to geoscientists with the general public can be challenging. Fostering and increasing public trust in geoscience is therefore essential to empower stakeholders to make better informed decisions about the various complex environmental issues facing society. There is abundant evidence that this can only be effective if there is a dialogue, an exchange of information with stakeholders, rather than just a presentation of information. However, before the geoscience community can effectively start that dialogue, we need to understand how the geoscience community is perceived, and how trusted it is. This project assessed the public perception of, and trust in, geoscience and geoscientists in Ireland, through a series of semi-structured interviews and a subsequent online survey. Trust in and the perception of geoscience and geoscientists was explored and compared to other professionals who may interact with the natural environment such as environmental scientists and engineers. The results from the survey, coupled with the thematic analysis of the semi-structured interviews provide a basis for reflection by the geoscience community.

A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.