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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

316
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:
316
Storage01:23

Storage

145
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
145
Multimachine Stability01:25

Multimachine Stability

246
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:
246
Energy Stored in a Capacitor: Problem Solving01:26

Energy Stored in a Capacitor: Problem Solving

1.2K
In 1749, Benjamin Franklin coined the word battery for a series of capacitors connected to store energy. Capacitors store electric potential energy that can be released over a short time. This property means capacitors have a wide range of applications.
Capacitor-discharge ignition is a type of ignition system commonly found in small engines where the energy released from a capacitor ignites an induction coil that, in turn, fires the spark plug.
To calculate the energy stored in a capacitor of...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Construction of TF-lncRNA-miRNA-mRNA Regulatory Network Affecting Sow Reproduction Based on QTLs for Corpus Luteum Number.

Animals : an open access journal from MDPI·2026
Same author

Sgf29 regulates pluripotency by maintaining chromatin accessibility and promoting the expression of core transcription factors.

Science China. Life sciences·2026
Same author

Insufficient telomeric DNA damage response promotes chromosomal instability in aged oocytes.

Science bulletin·2025
Same author

STING activation improves T-cell-engaging immunotherapy for acute myeloid leukemia.

Blood·2025
Same author

SLC25A1 and ACLY maintain cytosolic acetyl-CoA and regulate ferroptosis susceptibility via FSP1 acetylation.

The EMBO journal·2025
Same author

PTPN23-dependent activation of PI3KC2α is a therapeutic vulnerability of BRAF-mutant cancers.

The Journal of experimental medicine·2025

Related Experiment Video

Updated: Oct 1, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep

Zhuofu Deng1, Xianglong Qi1,2, Tengteng Xu1

  • 1Software College, Northeastern University, Shenyang, Liaoning 110 169, China.

Computational Intelligence and Neuroscience
|March 3, 2022
PubMed
Summary

This study introduces a new electricity price forecasting strategy to improve battery energy storage system scheduling. The novel method enhances prediction accuracy, leading to better economic benefits for energy storage operations.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Related Experiment Videos

Last Updated: Oct 1, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Area of Science:

  • Electrical Engineering
  • Energy Systems Analysis
  • Artificial Intelligence

Background:

  • Electricity prices are volatile and crucial for energy market participants.
  • Accurate electricity price forecasting is essential for optimizing energy storage system management.
  • Existing forecasting models struggle with the inherent uncertainty of electricity prices.

Purpose of the Study:

  • To propose a novel electricity price forecasting strategy for optimizing battery energy storage system (BESS) scheduling.
  • To enhance the accuracy and reliability of electricity price predictions in dynamic energy markets.
  • To improve the economic performance of BESS operations within microgrids.

Main Methods:

  • Utilized multiple non-stationary decompositions to extract significant price series components.
  • Employed a deep convolutional neural network with multiscale dilated kernels for multistep price forecasting.
  • Integrated advanced price fluctuation detection for optimized BESS operation in Ontario grid-connected microgrids.

Main Results:

  • The proposed strategy demonstrated superior performance compared to state-of-the-art methods in electricity price forecasting.
  • Extracted components from price series showed discriminative features crucial for accurate regression prediction.
  • The approach showed a promising prospect for improving the economic benefits of BESS.

Conclusions:

  • The novel electricity price forecasting strategy effectively addresses the uncertainty in electricity markets.
  • The method provides a significant improvement over existing techniques for BESS scheduling.
  • This research offers a promising pathway for enhancing the economic viability of battery energy storage systems.