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Related Concept Videos

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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:
Load-frequency control01:28

Load-frequency control

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...
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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.
Turbine-Governor Control01:17

Turbine-Governor Control

Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...

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Related Experiment Videos

Wind forecasting-driven profit optimization in deregulated energy markets using long short-term memory and American

Shreya Shree Das1, Muhammad Waseem Khan2, Subhojit Dawn3

  • 1VIT-AP University, Amaravati, Andhra Pradesh, India.

Scientific Reports
|June 24, 2026
PubMed
Summary

This study introduces a novel techno-economic model using Long Short-Term Memory (LSTM) for wind forecasting and American Zebra Optimization (AZO) for profit maximization in renewable energy markets, significantly reducing price imbalances and enhancing profitability.

Keywords:
American zebra optimizationDeep learningImbalance pricingMachine learningProfitRenewable integrationWind forecasting

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Artificial Intelligence in Energy

Background:

  • Renewable energy sources (RES), especially wind power (WP), introduce operational uncertainties in deregulated electricity markets due to wind velocity variations.
  • Discrepancies between actual wind velocity (AWV) and forecasted wind velocity (FWV) lead to significant price imbalances (PIMB), impacting market scheduling, reliability, and profitability.

Purpose of the Study:

  • To develop a comprehensive techno-economic model for profit maximization in wind-integrated deregulated power systems.
  • To enhance wind forecasting accuracy and optimize operational decisions under market uncertainties.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) networks for accurate wind speed forecasting, capturing nonlinear temporal dependencies.
  • Integrated LSTM-based forecasting with an American Zebra Optimization (AZO) algorithm within an Optimal Power Flow (OPF) market framework.
  • Validated the model using real wind datasets from Oman and the IEEE 14-bus test system, considering Locational Marginal Pricing (LMP) and grid constraints.

Main Results:

  • LSTM demonstrated superior forecasting accuracy compared to traditional methods and Random Forest (RF).
  • The proposed framework reduced imbalance pricing by 28-35% and improved system profitability by approximately 6% (5.9-6.4%).
  • AZO algorithm outperformed Sequential Quadratic Programming (SQP) in convergence and profitability.

Conclusions:

  • The LSTM-based framework effectively models wind variability, reduces market uncertainty, and enhances renewable energy integration.
  • The developed model provides a robust decision-support tool for competitive electricity market operations under high RES penetration.