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<p>The high penetration rate of renewable energy sources (RESs) integrated into microgrid (MG) systems may adversely affect the frequency dynamics, due to the low inertia of the overall system, leading to critical frequency stability problems and MG collapse. This paper presents a comparative study investigating the performance of a virtual inertia control (VIC) scheme based on an optimized repetitive proportional-integral-derivative (RPID) controller using recent optimization algorithms. The proposed scheme is applied to control an islanded MG system to enhance the frequency dynamics and stability. Furthermore, the VIC loop is designed based on an RPID controller optimally tuned by osprey optimization algorithm (OOA), honey badger optimizer (HBO), subtraction-average-based optimizer (SABO), tunicate swarm algorithm (TSA), Jaya algorithm (JAYA), particle swarm optimization (PSO), and coati optimization algorithm (COT). Each optimization algorithm is used to find the optimal parameters of the RPID regulator that minimize the objective function defined as the integral time absolute error (ITAE) of the frequency deviation. Simulation tests are carried out in MATLAB/Simulink to assess the performance of the designed VIC with the optimized RPID controller based on the stated algorithms under random load disturbances and wind and solar energy variations. The numerical simulations are verified through real-time hardware-in-the-loop (HIL) testing based on the Opal-RT 4512 platform to validate the effectiveness of such a proposed technique, which shows superior performance in terms of disturbance alleviation with well-referenced tracking of all optimizers. More particularly, the outcomes indicate that the OOA, HBO, and JAYA algorithms-based control schemes exhibit substantial reductions in frequency variation (maximum frequency undershoot) compared to the COT, SABO, and PSO algorithms.</p>

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Virtual Inertia Control Based on Optimized Repetitive PID Controller Considering Recent Optimization Algorithms for MG Systems with Renewable Energy Penetration

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Abdelhak Hadjkaddour,
  • Ouahid Bouchhida,
  • Hani Benguesmia,
  • Aissa Chouder,
  • Larbi Chrifi Alaoui,
  • Ahmed Bendib,
  • Salwa Echalih

摘要

The high penetration rate of renewable energy sources (RESs) integrated into microgrid (MG) systems may adversely affect the frequency dynamics, due to the low inertia of the overall system, leading to critical frequency stability problems and MG collapse. This paper presents a comparative study investigating the performance of a virtual inertia control (VIC) scheme based on an optimized repetitive proportional-integral-derivative (RPID) controller using recent optimization algorithms. The proposed scheme is applied to control an islanded MG system to enhance the frequency dynamics and stability. Furthermore, the VIC loop is designed based on an RPID controller optimally tuned by osprey optimization algorithm (OOA), honey badger optimizer (HBO), subtraction-average-based optimizer (SABO), tunicate swarm algorithm (TSA), Jaya algorithm (JAYA), particle swarm optimization (PSO), and coati optimization algorithm (COT). Each optimization algorithm is used to find the optimal parameters of the RPID regulator that minimize the objective function defined as the integral time absolute error (ITAE) of the frequency deviation. Simulation tests are carried out in MATLAB/Simulink to assess the performance of the designed VIC with the optimized RPID controller based on the stated algorithms under random load disturbances and wind and solar energy variations. The numerical simulations are verified through real-time hardware-in-the-loop (HIL) testing based on the Opal-RT 4512 platform to validate the effectiveness of such a proposed technique, which shows superior performance in terms of disturbance alleviation with well-referenced tracking of all optimizers. More particularly, the outcomes indicate that the OOA, HBO, and JAYA algorithms-based control schemes exhibit substantial reductions in frequency variation (maximum frequency undershoot) compared to the COT, SABO, and PSO algorithms.