Centralized Load Research Articles

NUMERICAL MODEL ANALYSIS OF SUBGRADE SETTLEMENT WITH FOAM MORTAR REINFORCEMENT BASED ON THICKNESS VARIATION

Soft soils have low bearing capacity characteristics and can cause high settlement. Sub grade which becomes the lowest layer in road construction is expected to have a strong bearing capacity so that it can carry the construction load on it. To overcome this problem, it is necessary to reinforce it. Problems will arise if the thickness of the soft soil in the subgrade varies. So, it needs to know and identify the scale of declining that occurs if the soft soil variation is reinforced on it. Foam mortar is an alternative reinforcement that is expected to improve the properties of the soft soil. The purpose of this research is to analyze the soft soil embankment model using foam mortar reinforcement against variations in soft soil height. The method of analysis was carried out by numerical method of Plaxis 2D version 2023. The geometric model in this study with foam mortar thickness of 30 cm was varied against subgrade layer height of 60 cm, 120 cm and 180 cm. The modeled load is a centralized load with variations of 0, 10, 20, 40, 60, 80, 100 and 120 kN. Numerical results obtained the highest settlement occurred at 180 cm soft soil layer of 0.01421 mm. The largest deformation occurred in the soft soil layer of 1,461 x 10-3 mm. In conclusion, the thicker the soft soil layer, the higher the settlement and the greater the deformation.

Pursuing Optimization Using Multimodal Transportation System: A Strategic Approach to Minimizing Costs and CO2 Emissions

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO2 emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO2 emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability.

Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach

Serverless edge systems simplify the deployment of real-time AI-based Internet of Things (IoT) applications at the edge. However, the heterogeneity of edge computing nodes – in terms of both hardware and software – makes load balancing challenging in these systems. In this paper, we propose a performance-driven, empirical weight-tuning approach to achieve effective load balancing based on the characteristics and capabilities of the nodes. By extensively profiling the nodes, we gather knowledge on performance metrics such as throughput, energy efficiency, response time, AI accuracy, and cost. Using this acquired knowledge, we introduce a weighted round-robin strategy to optimize the performance metrics according to their observed significance. To address multiple objectives, we introduce a multi-objective method that aims to strike a balance between any arbitrary set of performance objectives simultaneously. Additionally, we explore a coordinated distributed approach to overcome the limitations of centralized load balancing. Next, we introduce Hedgi, a heterogeneous serverless edge architecture designed to efficiently configure and utilize the derived load balancing policies, validated empirically. To demonstrate the practicality of Hedgi, we containerize and serverlessize a real-time object detection application. Extensive empirical studies are conducted using Hedgi to evaluate the performance of the proposed load balancing approach. The results provide valuable insights into the design trade-offs of various load balancing policies and system designs in the heterogeneous serverless edge.

Low Carbon Economic Dispatch of Rural Distribution Network under High Photovoltaic Penetration

To promote the low-carbon development of the power grid, the penetration rate of distribution photovoltaics in power systems has greatly increased. Some distribution networks may switch roles between energy producers and energy consumers multiple times a day. Especially in rural areas with low loads and abundant land resources, a large number of distributed photovoltaics may increase the energy balancing burden of the transmission system. To address this, the optimal operation of local resources is the key to the local accommodation of photovoltaics. In rural distribution networks, the light industrial load and agricultural load in rural distribution networks have similar electricity consumption behavior, and are suitable for centralized load management. This article proposes a low-carbon economic dispatch model of rural distribution networks under high photovoltaic penetration.. In specific, the light industrial load and the agricultural load are modeled considering their production requirements. Carbon emission costs and photovoltaic consumption costs are incorporated into the dispatch model of the distribution network. The case study is conducted based on a rural distribution network. The results of the case study indicated that the proposed model could increase the photovoltaic absorption rate by 10.96% and reduce carbon emissions by 3.4% for the test system.

A novel approach for steelmaking scheduling with self-generation under real-time and demand charge tariffs

It is difficult for energy-intensive enterprises equipped with self-generation to incentivize consumers to shift load to low-demand periods through time-based pricing. This paper addresses a scheduling problem of steel plant equipped with self-generation under real-time electricity prices. Considering the possibility of centralized load shifting resulting in excessive loads for some periods, we also consider the demand charge pricing. A continuous-time mixed-integer linear programming model based on event expression and generalized disjunctive programming is proposed. As industrial scale problems cannot be solved by some commercial solvers in an acceptable time, a SPEA2 algorithm is tailored. Effective encoding based on decomposition-and-offsetting mechanism and decoding based on backward-and-rules are used to generate the initial population and scheduling scheme, respectively. Meanwhile, crossover and mutation operators incorporating conflict resolution strategies can strike a balance between exploring and exploiting to produce high-quality solutions. Numerical experiments indicate that the proposed approach has superiority in performance and efficiency.

Segmented Energy Routing for a Modular AC/DC Hybrid System

This paper presents a modular AC/DC system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge-organized medium-voltage (MV) DC links, with fully distributed low-voltage (LV) DC power ports. Copies of modules share the centralized load bus and interact with an MV AC grid in parallel. Hybrid-power-port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this paper, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed in order to realize fully autonomous energy routing involving MV AC grid, distributed PV generations, distributed battery storages, and LV load. The system is proved to be stable by using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

A Comprehensive Review of Load Balancing Techniques in Cloud Computing and Their Simulation with CloudSim Plus

Background: The field of cloud computing has been evolving for over a decade now. Load balancing is an important component of cloud computing. Load balancing implies scheduling of cloudlets (tasks) on virtual machines. Since this is an NP-hard problem, various heuristics for load balancing have been proposed in the research literature. The heuristics have been categorized, simulated and benchmarked in various ways; however, the information is scattered across many review articles. Objective: This review aims to bring a broad range of load balancing heuristics found in the research literature under one umbrella. It includes a comprehensive list of heuristics, a holistic set of criteria for their classification, and some key performance metrics and simulation tools used for their benchmarking. An illustration of a fair and comprehensive comparison of heuristics is provided using CloudSim Plus, a recent and advanced simulation tool. Method: The simulations performed with CloudSim Plus employ a generic model of task and machine heterogeneity with Poisson arrival of cloudlets and exponential distribution of cloudlet length to emulate actual cloud-computing scenarios. The simulation results in terms of key performance metrics are used to compare four centralized load balancing heuristics including Join Shortest Queue (JSQ), Join Idle Queue (JIQ), Round Robin and Minimum Completion Time (MCT).

Green Power Electricity Generation Devices Using Blockchain-Based Accent

Power storage systems offer numerous attractive characteristics of contemporary smart grids such as improved system stability, efficient demand response, and lower bills. Uncoordinated loading of ESUs, though is exhausting and can lead to an end of the power system. This paper uses blockchain and intelligent agreements to create a decentralised loading system with no need for a centralised load coordinator. The first ESU tokens can be used for authenticating itself anonymously in the blockchain. The ESU will give a request to charge the clever agreement address for the blockchain. This includes the state-of-charge (SoC), time to comprehensive chariots (TCC) and the quantity of charge required. This smart contract will then run the charge coordination process self-running, to postpone stresses for lower priority ESUs on potential time slots. ESUs with the top priorities are paid on the current slot. Each ESU should ensure the accurate measurement of charging schedules. Finally, our study indicates that performance costs can be appropriate in terms of gas usage while allowing de-central charge coordination, while increasing transparency, trustworthiness, and privacy conservation. We applied the projected gadget for the Ethereum test bed blockchain.

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a job-assignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

Joint Monitorless Load-Balancing and Autoscaling for Zero-Wait-Time in Data Centers

Cloud architectures achieve scaling through two main functions: (i) load-balancers, which dispatch queries among replicated virtualized application instances, and (ii) autoscalers, which automatically adjust the number of replicated instances to accommodate variations in load patterns. These functions are often provided through centralized load monitoring, incurring operational complexity. This article introduces a unified and centralized-monitoring-free architecture achieving both autoscaling and load-balancing, reducing operational overhead while increasing response time performance. Application instances are virtually ordered in a chain, and new queries are forwarded along this chain until an instance, based on its local load, accepts the query. Autoscaling is triggered by the last application instance, which inspects its average load and infers if its chain is under- or over-provisioned. An analytical model of the system is derived, and proves that the proposed technique can achieve asymptotic zero-wait time with high (and controlable) probability. This result is confirmed by extensive simulations, which highlight close-to-ideal performance in terms of both response time and resource costs.

Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems

PurposeLoad balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks.Design/methodology/approachTo achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration.FindingsThe experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment.Originality/valueDesign of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.

Load Balancing Routing for Wireless Mesh Network With Energy Harvesting

Centralized routing is beneficial as it provides a global network view to plan and achieve the best and effective path for real-time traffic. With the rapid development of software defined networking, centralized routing has aroused more and more concern of researchers. An effective centralized load balancing routing (LBR) for wireless mesh network considering energy harvesting is proposed in this letter. LBR can provide flexible and optimal routes for real-time traffic. To minimize the long-term weighted sum cost which includes load and energy condition, LBR considers the queue length, channel condition, energy cost and energy harvesting. The problem of making dynamic decisions based on real-time traffic conditions is solved by the dynamic programming approach. Simulation results demonstrate the advantages of LBR.

Hidden Markov Model-based Load Balancing in Data Center Networks

AbstractModern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.

Demand side management in smart grid: A laboratory-based educational perspective

Demand side management is an important operational aspect of smart grid focusing on cost and efficiency optimisation by load management and control. With growing power demand and shortage of energy resources, it is necessary to educate people on the benefits of demand side management. In the present Electrical Engineering curriculum, demand side management is also being taught as a core topic in power system courses. Hands-on laboratory sessional in demand side management will definitely assist the students in their understanding of the working principles and implementation challenges of demand side management. In this paper, a small-scale laboratory prototype demand side management system has been implemented and is being used in power system laboratory work for Electrical Engineering students at IIT Bhubaneswar (India). In this demand side management prototype system, a Central Energy Management Unit, interfaced with all load/energy control units at each customer end, is used for centralised load scheduling control. A data-entry circuit is used at each customer end for prior transmission of load scheduling request. For wireless data communication among various units, a Global System for Mobile communication is adopted. The laboratory setup consists of few loads, ARDUINO microcontrollers, relay modules, current sensors, MATLAB interface (at Central Energy Management Unit end) and Global System for Mobile communication modules. The learning performance/efficiency of the students has been evaluated by the students’ responses to a questionnaire before and after the experiment being conducted by them and it has been found to be noteworthy highlighting the educative efficacy of the well-designed experiment on demand side management.

Centralized coordination of emergency control and protection system using online outage sensitivity index

This paper determines the best locations for centralized load shedding considering the protection systems that can be monitored from transmission system to avoid cascading failures. The vulnerability of power system integrity is determined and ranked using online outage sensitivity index (OSI) considering ampacity of transmission lines, over excitation of generators and transformers, under frequency and under voltage protection relays and low voltage ride through capability of grid connected renewable energy sources. The power system elements are ranked based on the results of outage sensitivity index (OSI) to concentrate the load shedding as control action on areas containing most critical elements prone to outage. The efficiency of proposed multi objective approach is validated through numerical simulations of severe contingencies carried out in DigSilent Power Factory software on 39 bus IEEE standard test system.

Interval Confinement in Compression Zone to Evaluated Beams Performance Subjected Monotonic Loads

Confinement is one way that can be used to improve the performance of reinforced concrete structures, mainly related to ductility. The parameter of the distance between the confinement becomes an important thing that must be studied its effect on ductility produced by a structural element. This study aims to study the effect of different distance between the confinement in compression zone in the beam at the plastic hinge area to the displacement and the behavior of the beam when it was given monotonic loading. The specimen model which is a simplified form of the plastic hinge area up front column will be fitted with a confinement in the compression zone which is attached to the shear reinforcement with different distances of 0, 70, 125 mm. Also made a beam with a crossties confinement spaced 125 mm for comparison. The presence of a centralized load in the middle of the span is intended to obtain the largest moment and shear areas in the plastic hinge. The test results showed that the installation of 125 mm intervals for confinement in the compression zone resulted in a higher ductility of 11-18% against the beam without confinement than the 70 mm interval which only increased by 2.78%. The hoops confinement produces higher ductility than crossties confinement for the same confinement interval. The increased confinement interval from 70 to 125 mm not yet significantly affect the moment capacity of the beam and the collapse was still dominant in the bending collapse although the distance between the stirrups and the confinement was slightly widened.

LMI-Based Robust Predictive Load Frequency Control for Power Systems With Communication Delays

This paper presents a robust predictive load frequency control for power systems with uncertain parameters and time delays in communication networks. The goal of the proposed approach is to achieve good performance for the closed-loop system under practical problems of the network including uncertainties in the dynamic model, time delays in the system, and time-varying model. To this end, a decentralized state-feedback control law is obtained by solving an linear matrix inequality based optimization. The aim of the optimization problem is to regularize the frequency deviation with the minimum control effort. It is shown that the stability of the system is guaranteed with respect to the Lyapunov stability theorem. Moreover, the problem is reformulated as a centralized load frequency control (LFC) approach for single-area power systems, and also as a non-predictive LFC method with lower computational complexity. The performance and robustness of the proposed strategy are studied through simulation results in different cases of uncertain and time-varying single-area and multi-area power systems with time delays.

Load Following of Multiple Heterogeneous TCL Aggregators by Centralized Control

Aggregate thermostatically controlled loads (TCLs) are good candidates for providing load following services in power systems. This paper is concerned with the modeling, evaluation, and control problems of a population of heterogeneous TCLs. Specifically, the heterogeneous population is divided into multiple homogeneous clusters and each cluster, i.e., TCL aggregator, is modeled by an approximated three-input single-output state space model. Here, the aggregators serve as a bridge connecting the load utility and the terminal TCLs, which have their own decision makers and are responsible for aggregate estimation and command issuing. And aggregate evaluation is carried out for the aggregator so as to provide the aggregate regulation capacities and ramping rates, which is useful for setting of the reference power trajectory. Based on the established control model, we furthermore propose a hierarchical centralized control algorithm for a bus load utility to regulate all TCLs inside it so as to provide load following service, while not affecting the customers’ comfort levels. Finally, simulation results with respect to a common bus load are provided to demonstrate the effectiveness of the proposed aggregate modeling and the centralized load following strategy.

Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks

This work proposes a Software Defined Networking (SDN) solution to address Wi-Fi congestion due to an unevenly distributed load among access points (APs). The conventional methods generally let client stations learn of APs’ load status and select APs distributively. However, such a client-driven approach lacks a global view to make precise load balancing decisions and may result in repeated changes in client-AP association. Although several studies proposed more efficient network-controlled methods to carry out Wi-Fi load balancing, some of them are distributed methods incurring excessive message exchange among customized APs, while the rest centralized methods are found to burden the central controller with unnecessary AP association decisions. In contrast, our solution adopts standardized OpenFlow protocol and SDN controller technology to Wi-Fi networks, organizing the SDN controller and the APs into a two-tier architecture so that the controller can evaluate the degree of load balancing among the APs and decide up to which load level the APs can accept association requests without consulting the controller. From our experiment results, our solution improves Wi-Fi’s load balancing degree by 34–41%, and yields an improvement of 28–36% in Wi-Fi’s re-association time over generic centralized load balancing methods with positive control.

Optimal Energy Management for Stable Operation of an Islanded Microgrid

This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of $\pm 5\%$ recommended by the standard ANSI C84.1-1989.