Jove
Visualize
Contact Us

Related Concept Videos

Distributed Loads01:19

Distributed Loads

1.0K
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...
1.0K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
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...
1.2K
Multimachine Stability01:25

Multimachine Stability

589
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
589
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.0K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.0K
Thermodynamic Systems01:06

Thermodynamic Systems

8.8K
A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
8.8K
Heating and Cooling Curves02:44

Heating and Cooling Curves

28.3K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
28.3K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Path Integral Diffusion: AdaPID.

Entropy (Basel, Switzerland)·2026
Same author

U-Turn Diffusion.

Entropy (Basel, Switzerland)·2025
Same author

Lagrangian large eddy simulations via physics-informed machine learning.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Statistical mechanics of thermostatically controlled multizone buildings.

Physical review. E·2023
Same author

Prediction and prevention of pandemics via graphical model inference and convex programming.

Scientific reports·2022
Same author

Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation, and Optimal Measurement Placement in Power Systems?

Frontiers in big data·2021
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 Experiment Video

Updated: Feb 24, 2026

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K

Ensemble of Thermostatically Controlled Loads: Statistical Physics Approach.

Michael Chertkov1,2, Vladimir Chernyak3

  • 1Center for Nonlinear Studies & T-4, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. chertkov@lanl.gov.

Scientific Reports
|August 19, 2017
PubMed
Summary
This summary is machine-generated.

This study analyzes how aggregated thermostatically controlled loads, like air conditioners, can provide demand response services. Statistical physics reveals how device switching policies impact grid stability and energy consumption.

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K
Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.1K

Related Experiment Videos

Last Updated: Feb 24, 2026

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K
Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.1K

Area of Science:

  • Statistical Physics
  • Energy Systems Engineering
  • Computational Physics

Background:

  • Thermostatically controlled loads (TCLs), such as air conditioners and heaters, are major electricity consumers.
  • These devices typically use bang-bang control, switching on/off based on temperature thresholds.
  • The aggregation of numerous TCLs presents opportunities for grid services like demand response.

Purpose of the Study:

  • To analyze the relaxation dynamics of aggregated TCLs using statistical physics.
  • To establish a relationship between ensemble relaxation and probability flux statistics.
  • To understand how switching policies influence oscillatory trends and relaxation speed in TCL ensembles.

Main Methods:

  • Utilized theoretical and computational tools from statistical physics.
  • Analyzed the relaxation of a large group of similar TCLs into a stationary distribution.
  • Investigated the probability flux statistics in a mixed phase space (discrete switching, continuous temperature).

Main Results:

  • Derived the non-equilibrium statistical system spectrum.
  • Established a link between relaxation dynamics and probability flux.
  • Identified how switching policies affect oscillatory behavior and relaxation rates.
  • Demonstrated the ensemble's recovery from perturbations.

Conclusions:

  • Statistical analysis of TCL ensembles provides insights into demand response capabilities.
  • Switching policies significantly influence the dynamic response and stability of aggregated loads.
  • This framework can guide the development of advanced demand response technologies for grid balancing.