Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

164
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.
164
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

697
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...
697
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

724
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
724
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

270
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:
270
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

3.7K
Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
3.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

Sensors (Basel, Switzerland)·2024
Same author

Research on Data Poisoning Attack against Smart Grid Cyber-Physical System Based on Edge Computing.

Sensors (Basel, Switzerland)·2023
Same author

Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment.

Sensors (Basel, Switzerland)·2022
Same author

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data.

Social network analysis and mining·2022
Same author

Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming.

Sensors (Basel, Switzerland)·2022
Same author

Vaccine versus Variants (3Vs): Are the COVID-19 Vaccines Effective against the Variants? A Systematic Review.

Vaccines·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 23, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

580

A Bilevel Optimization Model Based on Edge Computing for Microgrid.

Yi Chen1,2,3, Kadhim Hayawi4, Meikai Fan5

  • 1College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a bilevel optimization model for microgrids, integrating edge computing for cost-optimal control. This approach enhances smart grid efficiency and load balancing in complex power systems.

Keywords:
costedge computingmicrogridoptimizationpower distribution

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

606
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.1K

Related Experiment Videos

Last Updated: Aug 23, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

580
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

606
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.1K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Renewable Energy Systems

Background:

  • Increasing complexity of power systems due to renewable energy integration and microgrid expansion.
  • Need for efficient, low-delay data processing in smart grids.
  • Deployment of edge computing in microgrids for enhanced control.

Purpose of the Study:

  • To present a bilevel optimization control model for microgrids.
  • To achieve cost-optimal control decisions under load balancing.
  • To optimize power distribution costs within microgrids.

Main Methods:

  • Development of a two-layer optimization model: an upper-level optimal control module and a lower-level optimal control module.
  • Upper-level module sets decision variables for the lower-level module.
  • Lower-level module uses mathematical methods to find optimal solutions and feedback to the upper-level.

Main Results:

  • Demonstration of the bilevel optimization model's feasibility through experimental validation.
  • Effective optimization of microgrid power distribution costs.
  • Integration of edge computing modules for enhanced system performance.

Conclusions:

  • The proposed bilevel optimization model effectively manages microgrids with integrated edge computing.
  • The model achieves cost-optimal control and load balancing in complex power systems.
  • Experimental results confirm the model's viability for smart grid applications.