Power Management In Networks Research Articles

Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems

Optimizing Electric Vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

Joint wireless energy transmission and dynamic power management for full-duplex relay system

The rapid deployment of fifth-generation (5G) mobile communication systems demands sustainable power solutions. This paper introduces a novel joint design for wireless energy transmission and dynamic power management in full-duplex relay systems, aiming to ensure a continuous power supply for relay nodes. Our approach integrates energy harvesting from RF signals and dynamic battery management, focusing on optimizing throughput via energy allocation and precoding design. Simulation results demonstrate that our scheme significantly outperforms conventional non-battery half-duplex schemes in system throughput. Notably, we observe performance increments in throughput compared to all mentioned traditional methods, highlighting the efficacy of our design in enhancing 5G network sustainability and efficiency. This innovative approach presents a promising solution for effective power management in next-generation mobile networks.

Reactive power management in distribution networks in the presence of distributed generation sources based on information gap decision theory

The presence of uncertain parameters in power systems has led to many challenges for the designers and operators of these systems. One of these challenges is reactive power management in the presence of distributed renewable generation sources.In this article, the management of reactive power in distribution networks in the electricity market and the presence of distributed renewable generation sources, including wind and solar power plants, is performed considering the uncertainties in the network load, power generation of distributed generation sources, and active and reactive power market prices. Furthermore, reactive power cost modeling of reactive power compensation equipment is carried out.A hybrid stochastic/robust optimization method is employed to model the uncertainties in the problem. Finally, the efficiency of the method is confirmed by numerical examinations using the IEEE 33-bus distribution network and the GAMS optimization software. Simulation results indicate that in the risk-averse strategy, for a certain increase in cost, the radius of uncertainty in the active and reactive power market prices increases. Also, in this strategy, as β increases, the total cost of network operating increases by 81.72 %, while in a risk-seeking strategy, with the increase of β, the total operating cost of the network decreases by 77.78 %.

Incentive-based fault-tolerant cooperative payoff design in weakly acyclic games

This paper proposes an incentive-based cooperative payoff design method to accommodate faults in weakly acyclic games, where the payoff functions of normal players are designed such that all players’ optimal payoffs are recovered into an admissible level. Besides, the stochastically stable Nash equilibrium (SSNE) is regarded as the optimal state to avoid some inefficient Nash equilibria. Firstly, without cooperation among players, the effect of faults on SSNE and the optimal payoff is analyzed deeply. Further, the conditions of fault accommodation are discussed under the proposed cooperative fault tolerance method, based on which an algorithm is designed to calculate the feasible incentive. Secondly, with cooperation among players, the effect of faults is analyzed again in special weakly acyclic games, namely potential games, and the simpler conditions of fault accommodation are provided. Finally, two examples of frequency assignment in distributed systems and power management in sensor networks illustrate the effectiveness of the proposed methods.

Integrating life cycle sustainability assessment in power flow optimization

The electric power network management is an important field of improving to achieve sustainability. Optimal Power Flows (OPFs) allow the efficient operation of the system. However, OPF algorithms only consider the operation stage of the power plants involved in the power system, neglecting the rest of stages of its life cycle. To evaluate the total effects of the power generation mix, this work develops a methodology to perform a Multi-Objective OPF (MOOPF) with impact variables in electrical systems that integrate different generation technologies. These variables result from the application of a Life Cycle Sustainability Assessment (LCSA), consisting in environmental, economic, and social indexes with “cradle-to-grave” dimensions. To achieve this objective, the MOOPF is carried out operating a MATLAB algorithm over the IEEE57 bus system. It uses the ɛ-constraint method subject to the imposition of two or three impact variables. To examine the applications of this work, several simulations are performed optimizing different impact variables. Two different Spanish electricity scenarios are considered: the 2019 generation, and the 2030 objective generation proposed by the Spanish National Energy and Climate Plan. This tool broadens the scope of MOOPF assessment that can be used in a decision-making process. It allows to identify important environmental, economic, and social affections derived from system designs and limits. The study proves that the application of LCSA with MOOPF obtains realistic results making sustainability solutions technically viable. Additionally, different benefits and drawbacks, linked to the studied scenarios and grid disposition, are compared when conducting the proposed methodology for hourly generation management.

Relay node selection scheme and deep sleep period for power management in energy‐harvesting wireless sensor networks

SummaryThis paper presents Relay node selection scheme and Deep sleep period for power management in Energy Harvesting Wireless Sensor Networks (RD‐EHWSN), a new energy‐saving scheme founded on asynchronous duty cycling. RD‐EHWSN reduces sensor node energy consumption and guarantees equilibrium energy use between sensor nodes in WSN with the energy harvesting capacity by adjusting these sensor nodes duty cycles more drastically and deeply by according to the estimated value of its residual energy on the basis of future‐presented harvested energy, and this is done through the use of a new proposed energy threshold policy. RD‐EHWSN also grips the benefit of transmitter initiated using the low power listening (LPL) technique with short preamble messages and uses a new relay node selection procedure to achieve the load balancing in WSN. We implemented RD‐EHWSN by using OMNeT++/MiXiM. For evaluation, we compared it with PS‐EHWSN, under multiple concurrent multihop traffic flows scenarios and scenarios in which nodes can harvest different energy harvesting rate. In all experiments, RD‐EHWSN significantly outperformed the PS‐EHWSN scheme; the results of simulation demonstrate that our scheme enhances the general yielding of WSN thru lessening the energy consumption and the mean latency, as well as raising the packet delivery ratio and the throughput. Moreover, RD‐EHWSN improves the WSN lifetime and ensures it operates in good condition in the case where the energy harvesting rate is lower by comparing it with the PS‐EHWSN scheme.

Optimal management of power networks using a dynamic line rating approach

Due to the stochastic nature of wind, the wind power integration into the power system poses serious challenges to the transmissionsystem operators(TSO). The impact of large amounts of windenergygeneration onto the power system may congest some of the transmissionlines that transport it to the (sometimes)distant consumption centres. Since the occurrence of wind not only contributes to the loading of theconnecting electric line, but also increases the line capacity, via convective cooling of the cables, a dynamic line rating (DLR)analysis computes a more realistic set of values for the line capacity,thusit can be a cost-effective solution toalleviatesomeoverhead line congestion problems.This work presentsan operational tool for the DLR analysis of power networks,allowingthe optimal integration of renewable energy sources,especially wherepotentially congested linesmay exist. The tool was appliedto a real case studyusing forecast meteorological data, and the results achieved were compared with those obtained by using the Portuguese TSOmethodfor assessingthe transmission capacity of the lines. For high wind speed conditions,results show anoticeable increase on the cable’s convective cooling assessed by the DLR analysis. This assessmentleadsto a noticeable rise on the cables’capacitiesthat overcame the congestion associated with the high injection levels of wind power generationin the power grid.

Wind power error compensation prediction model based on CEEMD-SE-ELM-TCN

Abstract Wind power generation holds immense importance in addressing the issue of global energy shortage, while precise wind power forecasting proves essential for effective management and dependable operation of wind power networks. This study introduces a hybrid deep learning model, encompassing complete ensemble empirical mode decomposition (CEEMD), sample entropy (SE), extreme learning machine (ELM) and time convolutional network (TCN), for accurately predicting short-term wind power output. First, CEEMD decomposed the original wind power into multiple submodes, which effectively reduced the series volatility. Then, the SE of intrinsic mode function sequence is calculated, and the subsequences with similar complexity are superimposed to reduce the calculation cost, improve the simulation accuracy and reduce the noise of the original wind power sequence. Secondly, the ELM model is established for each submode, and the prediction error of BiLSTM is predicted again using TCN to improve the efficiency and prediction performance of the hybrid model. Finally, the outcomes of each individual submode are amalgamated to yield the ultimate prediction outcome. To showcase the efficacy and dominance of the error compensation technique, several comparison models were established in the experiment. The results demonstrated that the suggested hybrid model exhibits superior predictive accuracy in the domain of wind power prediction. Compared with the comparison model, the improvement in MAPE and RMSE was 60.50 and 77.74%, respectively.

Cost analysis of ultrawideband transmission in optical networks

We examine the cost associated with increasing traffic on a reference transport network for both single- and multiband systems, with a total transmission bandwidth ranging from 4.8 to 20 THz (S2 + S1 + C + L-band, where S2 and S1 are the two halves of the S-band). The number and cost of amplifiers, transceivers, and fibers are considered. To ensure stable transmission power and simplified network management, we take noise loading into account. The optimal combination of transmission bands—for each system—is evaluated based on the maximization of the capacity calculated from the minimum per-band generalized signal-to-noise ratio of each band. The results reveal that, despite the lower spectral efficiency due to the higher fiber attenuation and band MUX/DEMUX insertion losses, multiband systems can effectively reduce the number of fibers needed, thus postponing the deployment of new fibers. The most cost-effective solution depends on the fiber and S-band amplifier costs and the forecast of the required network capacity. When additional fibers are freely available or fiber costs are minimal, employing a C-band-only system proves to be the most economical choice. As fiber costs increase, additional bands gradually become more appealing, progressing from the C-band-only to the C + L-band, and then to the addition of half of the S-band (S1 + C + L-band) or the whole S-band (S2 + S1 + C + L). Furthermore, multiband transmission systems are more attractive when considering higher traffic loads. We also propose an optimal order of deployment of transmission bands in a pay-as-you-grow approach. The corresponding cost evolution demonstrates that this approach is a viable solution to mitigate excessive costs during the initial stages of a network operation, even though it leads to an increased number of fibers and transceivers used before all transmission bands are enabled. Additionally, we show that the S1- and S2-bands have worse optical performance, making them more suitable for shorter lightpaths. Consequently, operating the S-band in single-hop links and simplifying the add-drop multiplexer architecture could be beneficial.

Retraction Notice: A Robust Integrated Approach for Optimal Management of Power Networks Encompassing Wind Power Plants

Retraction Notice: A Robust Integrated Approach for Optimal Management of Power Networks Encompassing Wind Power Plants

Optimal placement of the phasor measurement units using differential evolution algorithm

The increasing consumption of the electric energy aimed at develop the transmission networks and the demand for higher reliability from the network; in this regard, wide-area measurement systems using phasor measurement units (PMUs) have revolved the trend of power network management. In this paper, the optimal allocation of PMUs in order to reach the perfect observability of the network; based on a differential evolution algorithm, is proposed and it is shown that, the deployment of constraints related to the zero-injection busses (ZIB) aimed to decrease the number of PMUs and their corresponding cost. By comparing the proposed method to the other methods, its simplicity and good performance are approved.

PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks

Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers. With the advent of renewable power resources and EVs resulting into uncertain generation and highly dynamic load demands, it has become ever so important to ensure robust operation of power networks through suitable management of transient stability issues and localize the events of blackouts. In the light of ever increasing stress on the modern grid infrastructure and the grid operators, this paper presents a reinforcement learning (RL) framework, PowRL, to mitigate the effects of unexpected network events, as well as reliably maintain electricity everywhere on the network at all times. The PowRL leverages a novel heuristic for overload management, along with the RL-guided decision making on optimal topology selection to ensure that the grid is operated safely and reliably (with no overloads). PowRL is benchmarked on a variety of competition datasets hosted by the L2RPN (Learning to Run a Power Network). Even with its reduced action space, PowRL tops the leaderboard in the L2RPN NeurIPS 2020 challenge (Robustness track) at an aggregate level, while also being the top performing agent in the L2RPN WCCI 2020 challenge. Moreover, detailed analysis depicts state-of-the-art performances by the PowRL agent in some of the test scenarios.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately, in modern smart power grids, energy demand for accurate forecast of electricity is gaining attention, with increased interest of research. This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand. In addition, proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network. As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman, new opportunities may arise considering the efficiency and reliability of the power system; like price-based demand response programs. These programs could either be a large scale for household, commercial or industrial users. However, excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure. Consequently, in this paper, an overview of the Oman regulatory regime, financial mechanism, price control, and distribution system security standard were presented. More so, the energy demand forecast in Oman was analysed, using the econometric model to forecasts its energy peak demand. The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters (by region, and by tariff), considering a case study of Mazoon Electricity Distribution Company (MZEC), which is one of the major power distribution companies in Oman, for effective energy demand in the power grid.

Optimizing Power Management in Distribution Networks: A Mathematical Modeling Approach for Coordinated Directional Over-Current Relay Control

Optimizing Power Management in Distribution Networks: A Mathematical Modeling Approach for Coordinated Directional Over-Current Relay Control

Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).

Efficient Reactive Power Management in Low Voltage Distribution Networks Using Pv Smart Inverters and Fc with Cost-Benefit Analysis

Efficient Reactive Power Management in Low Voltage Distribution Networks Using Pv Smart Inverters and Fc with Cost-Benefit Analysis

AI Models for Green Communications Towards 6G

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, there is no doubt that 6G will have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be widely adopted in 6G, further complicates the power control and network management. To address these challenges and reduce human intervention, Artificial Intelligence (AI) has been extensively recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present main considerations for green communications and survey related research on AI-based green communications. We focus on how AI techniques are adopted to manage networks and improve energy efficiency towards the green era. We analyze how Machine Learning (ML) techniques including state-of-the-art Deep Learning (DL) can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and improve the accuracy rate in 6G. Finally, we discuss the existing problems and envision the open research issues of AI models towards green 6G.

A Design and Realization of State Monitor Based on Avionics Core Processor

Based on the composition and the results of FMEA of Avionics Core Processor, this paper selects temperature, power and network for monitoring. Temperature monitoring for processor and high-power FPGA, current monitoring for power supply and network management and status collection. Next, the detailed design and implementation, information reporting and implementation process are discussed. Then the comprehensive diagnosis and system level state monitoring analysis are to be carried out in the next step.

Women in Diplomacy in Late Eighteenth-Century Istanbul

AbstractThis article identifies the different roles played by women in the diplomatic corps of the Pera embassies of Christian-ruled states. It focuses on women operating in and from the Habsburg embassy during the last two decades of the eighteenth century, a period marked by the revolutionary wars and the beginning of the ‘Eastern Question’. Using a microhistorical approach, this article analyses how women facilitated the embedding of individual members of the diplomatic corps in Pera's diplomatic social scene, the social integration of young diplomats, and the development of the trans-imperial networks of influence upon which diplomats heavily depended. It shifts the focus from states to actors and invites a more systematic development of a diplomatic history based on networks of non-official agents, thus enabling an improved understanding of the family, social, and urban dynamics that led to the development of political elites. This article draws on a set of private sources and parish sources in order to emphasize the role of households in the diplomacy of empires, the agenda of women in the management of patronage and power networks, and the diversity of their social affiliation.