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Related Concept Videos

Load-frequency control01:28

Load-frequency control

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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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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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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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Turbine-Governor Control01:17

Turbine-Governor Control

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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...
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Generator Voltage Control01:21

Generator Voltage Control

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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand, use...
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Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Updated: Jan 15, 2026

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Improving load frequency control in autonomous microgrid via Fick's law-based demand optimization.

Maloth Ramesh1, Anil Kumar Yadav2, Pawan Kumar Pathak3

  • 1Department of Electrical Engineering, Marwadi University, Rajkot, Gujarat, 360 003, India.

Scientific Reports
|October 15, 2025
PubMed
Summary

A new demand-contributed load frequency control (LFC) strategy using a Fick's Law Optimization (FLO) enhanced PI-(1+DF) controller stabilizes solar-wind autonomous microgrids. This method effectively manages complex dynamics and uncertainties for improved grid performance.

Keywords:
Demand contributionFick’s law optimizationMicrogrid systemOptimal load frequency controlPI-(1 + DF) controllerSolar-wind energy system

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy Systems

Background:

  • Autonomous microgrid systems (AMGS) integrating solar and wind power face frequency stabilization challenges due to intermittent generation and complex dynamics.
  • Conventional controllers struggle with uncertainties and nonlinearities like governor dead band (GDB) and generation rate constraints (GRC) in AMGS.
  • Demand-side resources, including electric vehicles (EVs), heat pumps (HPs), and freezers, offer potential for enhanced grid control but require sophisticated management.

Purpose of the Study:

  • To propose a novel demand-contributed load frequency control (LFC) strategy for frequency stabilization in solar-wind-based AMGS.
  • To develop and optimize a PI-(1+DF) controller using the Fick's Law Optimization (FLO) metaheuristic technique.
  • To evaluate the controller's performance against state-of-the-art algorithms under realistic operating conditions and parametric uncertainties.

Main Methods:

  • Implementation of a structurally enhanced proportional-integral controller with a one plus derivative filter (PI-(1+DF)).
  • Optimization of controller parameters using the physics-inspired Fick's Law Optimization (FLO) metaheuristic algorithm.
  • Modeling of the AMGS incorporating renewable sources (solar, wind), diesel engine generator (DEG), and flexible demand-side contributors, along with nonlinearities (GDB, GRC).

Main Results:

  • The FLO-optimized PI-(1+DF) controller significantly outperformed existing algorithms (MBA, SCA) in settling time and peak overshoot.
  • Simulations in MATLAB/Simulink demonstrated the controller's efficacy in maintaining frequency deviation within acceptable limits during severe disturbances.
  • Robustness tests with ±50% parametric variations confirmed the controller's resilience, showing minimal peak overshoots (e.g., 0.02 Hz) and undershoots (e.g., -0.957 Hz).

Conclusions:

  • The proposed demand-contributed LFC strategy, optimized by FLO, provides superior frequency stabilization for solar-wind AMGS.
  • The PI-(1+DF) controller demonstrates robust performance and adaptability in uncertain environments with significant parametric variations.
  • This approach offers a practical and effective solution for enhancing the stability and reliability of renewable energy-based microgrids.