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Related Concept Videos

Distributed Loads01:19

Distributed Loads

564
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Power System Distribution01:25

Power System Distribution

281
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
281
Load-frequency control01:28

Load-frequency control

219
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing.

Saurabh Singhal1, Senthil Athithan2, Madani Abdu Alomar3

  • 1Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India.

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Summary
This summary is machine-generated.

This study introduces Rock Hyrax Optimization (RHO) for smart grids, significantly reducing response time and energy consumption in cloud computing environments. The RHO algorithm optimizes virtual machine task assignment and power management for enhanced efficiency.

Keywords:
cloud computingenergy consumptionfog computingload balancingresource utilizationsmart grid

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Smart grids are increasingly adopting cloud-based systems, leading to massive data generation.
  • Cloud computing offers efficient services but faces challenges in resource management, response time, and energy consumption.
  • Fog computing enhances cloud capabilities by improving traffic management, security, and processing speed.

Purpose of the Study:

  • To propose a novel load-balancing approach for smart grids using Rock Hyrax Optimization (RHO).
  • To optimize response time and energy consumption in cloud environments within smart grids.
  • To reduce the overall operational costs and improve the efficiency of smart grid data centers.

Main Methods:

  • Development of a load-balancing algorithm based on Rock Hyrax Optimization (RHO).
  • Implementation of a task assignment strategy for virtual machines, including powering down idle resources.
  • Simulation and performance evaluation using the CloudAnalyst simulator.

Main Results:

  • The RHO algorithm demonstrated superior performance compared to static and dynamic load-balancing algorithms.
  • Achieved a 26% reduction in processing time and a 15% reduction in response time.
  • Resulted in a 29% decrease in energy consumption, 6% cost reduction, and 14% delay reduction.

Conclusions:

  • The proposed RHO-based load-balancing approach effectively optimizes smart grid performance.
  • Significant improvements in energy efficiency, response time, and cost-effectiveness were observed.
  • The RHO algorithm presents a viable solution for managing resources in cloud-based smart grid data centers.