Aug 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 714· 0 citations· 2 references
TL;DR
An AI-driven digital twin (DT) framework that pairs a real-time physical-data grid model with a layered AI optimisation engine made up of an LSTM/Transformer forecaster, a graph neural network (GNN) state estimator, and a deep reinforcement learning (DRL) dispatch controller is proposed.
Abstract
Growing reliance on distributed, weather-dependent renewable sources brings sizable variability, uncertainty, and stability problems to today's power grids. This work proposes an AI-driven digital twin (DT) framework that pairs a real-time physical-data grid model with a layered AI optimisation engine made up of an LSTM/Transformer forecaster, a graph neural network (GNN) state estimator, and a deep reinforcement learning (DRL) dispatch controller. The twin continually mirrors the physical grid, supporting scenario simulation, predictive fault detection, and closed-loop control recommendations before any action is applied to the real system. A case study on a modified IEEE 33-bus feeder with solar PV and wind generation shows the framework raises renewable utilisation by 19 points, cuts curtailment by 28 points, and lowers fault response time by 68.2% versus a conventional energy-management system. These results suggest AI-enhanced digital twins are a scalable route to self-optimising, resilient renewable-heavy grids.
Emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, are identified as promising strategies for developing resilient, intelligent, and sustainable future power grids.
Olatunde Ibiyinka, Tolu Omotoso, N. Ekekwe· Global Journal of Engineerin...· 0 citations
As solar and wind power are increasingly integrated into modern grids, intermittency and forecasting uncertainty pose a danger to system stability. The Machine Learning-Based Intelligent Energy Management System (ML-IEMS) proposed in this paper combines a hybrid CNN-LSTM model for short-term load and renewable generation forecasting with a Reinforcement Learning (RL) dispatch agent for real-time storage, demand response, and grid exchange scheduling. A five-layer architecture, a mathematical formulation of the power balance and cost objective, a Markov Decision Process (MDP) dispatch formulation, a thorough dataset, preprocessing, and evaluation protocol, as well as template result tables and comparative charts to facilitate empirical validation, are all provided. In comparison to statistical and rule-based baselines, the framework is anticipated to boost renewable usage, lower operational costs, and improve forecasting accuracy.
N. Suganthi, S. Tamilselvan· ITM Web of Conferences· 0 citations
Given the challenges associated with renewables-based power grids, such as greater volatility, greater non-stationarity, and traditional forecasting techniques failing to sufficiently adjust to adaptive changes, this paper studies the design and implementation of an adaptive power grid load forecasting model suitable to integrate renewables in power grids. We used multi-source load data as the basis of data engineering and pre-processed the data to create a unified load, photovoltaic (PV), wind, meteorology and time encoded data vector. We designed a load forecasting model to use a combination of dilated convolution, Transformer encoding, and encapsulated perturbation gating, as well as an optimized training approach based on a weighted loss function, adaptive dynamic learning rates, and residual correction. Our approach achieved best results when compared to other models in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), as well as provided stable forecasting results in high PV penetration forecasting conditions, forecasting conditions with sudden changes in wind, and meteorological conditions forecasting with high volatility. This study provided a multi-source time-series forecasting method.
Unknown authors· European Conference on Elect...· 0 citations
The study concludes that reinforcement learning will be a highly robust and versatile solution to next-generation optimization of cyber grids with regard to data-based and autonomous, data-based grid management systems.
Fatima Zahra El Idrissi· International Journal of App...· 1 citation
The rapid growth of renewable energy integration has increased the complexity of smart grid operation due to the intermittent nature of distributed energy resources and continuously varying load demand. Existing approaches often rely on centralized control or combine only selected intelligent technologies, limiting scalability, data privacy, and autonomous decision-making. This paper proposes a digital twin-driven federated multi-agent intelligence (DT-FMAI) framework that integrates virtual system synchronization, privacy-preserving distributed learning, and cooperative multi-agent control within a unified architecture. Digital twins continuously mirror physical grid assets, federated learning enables collaborative forecasting without sharing raw data, and intelligent agents coordinate energy management in real time. The framework was implemented in MATLAB/Simulink with TensorFlow Federated and evaluated using renewable generation, weather, battery, and load datasets. Results demonstrate a 15-25% reduction in forecasting error, 10-18% improvement in voltage regulation, 92-96% load-matching efficiency, and 12-20% higher energy efficiency. These outcomes demonstrate the potential of the proposed framework for scalable, secure, and intelligent renewable-integrated smart grid operation.
Unknown authors· International Journal of Pow...· 0 citations
The increasing penetration of weather-driven renewable energy sources in smart grids introduces operational instability, harmonic distortion, and elevated switching costs due to the limitations of rule-based and deterministic control strategies. This study proposes a deep reinforcement learning-based adaptive switching framework to enhance renewable utilization while minimizing operational risk and economic cost. A simulation-derived dataset incorporating renewable generation, load demand, total harmonic distortion, voltage deviation, frequency variation, and risk–cost indices was generated from a risk–cost optimized smart grid model and implemented in Google Colab. The switching problem was formulated as a Markov decision process with a state space composed of power quality and economic variables, and a discrete action space representing operational modes. A Deep Q-Network agent was trained over 24-hour episodes to learn optimal switching policies. Comparative evaluation against conventional, rule-based, and analytical risk–cost optimization strategies demonstrated up to 14% reduction in total harmonic distortion, 22% reduction in switching frequency, 17% reduction in operational cost, and 19% improvement in risk mitigation, while increasing renewable penetration by 11%. The proposed framework provides a scalable and intelligent solution for industrial smart grid applications.
M. Meyyappan, P. Avirajamanjula, P. Marimuthu et al.· ITEGAM- Journal of Engineeri...· 0 citations
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