Electrical and Electronic Engineering, Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Control and Systems Engineering
Digital twin assisted of frequency stability enhancement in Indian smart grids with EV charging infrastructure along with quantum optimization M. Kaleeswari, P. Sivakumar, A. Aswini Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2026 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 (T s ) 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.
Deep neural network based digital twin framework for per-day energy extraction in partially illuminated photovoltaic systems Aswini A, Sivakumar P, Kaleeswari M Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2025 Digital Twins (DTs) in digitalization have become a potent device in real-time optimization of the photovoltaic (PV) system. Partial shading in PV systems is a serious issue resulting in significant energy wastage. The methodology discussed in this paper is to maximize the per-day energy extraction (PDEE) in the case of shading through the utilization of a supplementary PV source. The methodology combines two important features: (1) Digital Twin Framework design for PV Systems Using Calibrated Voltage, Current, Temperature, and Solar insolation Data to facilitate reliable online power assessment with reduced dependence on hardware sensors, thereby improving scalability and maintainability. (2) Incorporating external biasing units in series to mitigate partial shading effects in PV arrays to maintain V m ref across all PV arrays to optimize the overall power retrieval. A PV system’s digital twin is a fusion of analytical Formulation of the Photovoltaic Model and deep neural networks (DNNs) optimized using the improved Harris Hawks Optimization (IHHO) algorithm. Moreover, a machine learning model can be applied to the Digital Twin features to predict the Global maximum reference voltage. Simulations and experimental findings indicate an increase in power extraction by 15.4%, which points to a viable approach to reduce the effect of shading in a realistic PV system.
