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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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Improved Lyrebird optimization for multi microgrid sectionalizing and cost efficient scheduling of distributed

Karthik Nagarajan1, Arul Rajagopalan2, Mohit Bajaj3

  • 1Department of Electrical and Electronics Engg, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India.

Scientific Reports
|May 19, 2025
PubMed
Summary

This study introduces an Improved Lyrebird Optimization Algorithm (ILOA) for optimizing multi-microgrids, reducing energy costs and power losses. ILOA enhances efficiency and reliability in power systems, outperforming existing algorithms.

Keywords:
Active power lossDistributed generatorsEnergy index of reliability (EIR)Generation cost minimizationLyrebird optimization algorithmMulti-microgridsMulti-objective optimizationOptimal scheduling

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Smart Grid Technology

Background:

  • Rising energy demand and grid inefficiencies necessitate advanced solutions.
  • Microgrids (MGs) with distributed generation (DG) offer improved reliability and flexibility.
  • Current optimization methods face challenges in balancing cost, loss, and reliability.

Purpose of the Study:

  • To introduce the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems.
  • To minimize generation costs and active power losses while ensuring system reliability.
  • To evaluate ILOA's performance against standard algorithms in single and multi-objective scenarios.

Main Methods:

  • Developed ILOA by integrating Levy Flight for enhanced local search and a chaotic sine map for global search.
  • Applied ILOA to a modified 33-bus distribution system segmented into three microgrids.
  • Evaluated performance in single-objective (cost/loss minimization) and multi-objective optimization, including reliability constraints (Energy Index of Reliability - EIR).

Main Results:

  • ILOA achieved superior results in both single-objective (e.g., $19,254.64/hr cost, 0.7118 kW loss) and multi-objective optimization (e.g., $89,792.18/hr cost, 10.26 kW loss).
  • Demonstrated significant reductions in operation costs and power losses compared to Lyrebird Optimization Algorithm (LOA), Jaya Algorithm (JAYA), and Genetic Algorithm (GA).
  • Incorporation of EIR constraints further validated ILOA's robustness and efficiency in minimizing power loss.

Conclusions:

  • ILOA is a highly efficient and reliable algorithm for multi-microgrid sectionalizing and distributed generation scheduling.
  • The algorithm shows significant potential for real-world smart grid applications, including dynamic economic dispatch and demand response.
  • ILOA offers improved exploration-exploitation balance and global search capability, surpassing conventional optimization techniques.