The amalgamation of Electric Vehicle Charging Station (EVCS) along with high penetration of green energy in Distributed Generation (DG) system affects the frequency stability, tie-line power fluctuations, and instability due to the load pattern variations of industries and different charging patterns at EVCS, and the intermittent sources variations like different wind velocity, and solar irradiance levels. Due to this, there will be a lack of coordination between the load and power demand. Implementation of Load Frequency Controller (LFC) with the DG system will reduce earlier stated problems. However, in this proposed research, LFC is tuned using a quantum-inspired optimization methodology for fast-varying load patterns and source variations in a complex DG system. This paper presents Quantum Enhanced Gorilla Troop Optimization (QEGTO), and performance is compared with other quantum-inspired optimization methodologies. In these assessments, the proposed work with the QIEGTO method gives superior stability in terms of settling time (Ts) and Integral Time Absolute Error (ITAE) for step and random load variations. For different percentages of load pattern variations and intermittent source variations, QIEGTO attains a 20% faster settling time with 40% decrease in steady-state error. Digital Twin model framework analysis satisfies the virtual real-time replica and predicts the frequency variations with neural network along with QIEGTO.
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