ARTIFICIAL INTELLIGENCE–ENABLED SMART GRIDS: A UNIFIED FRAMEWORK FOR OPTIMAL ENERGY DISTRIBUTION, FAULT DETECTION, AND DEMAND FORECASTING

Abstract

The emergence of renewable energy resources, more highly distributed generation and electrification of end use sectors have radically shaken the reliability and efficiency of the traditional power grids. Smart grids augmented with AI have been developed as a potential solution to predictive, adaptive and resilient operation. It examines how three recent AI technologies- deep reinforcement learning (DRL) applied to the energy distribution optimization problem, convolutional neural network -long short-term memory (CNN -LSTM) hybrids are used in fault detection and Temporal Fusion Transformer (TFT) is applied to the short- and mid-term demand forecasting- perform in two benchmark IEEE-33 and IEEE-123 distribution feeders. Simulation test results show that DRL minimises voltage violations by more than 70 percent, CNN-LSTM reports fault classification accuracy above 98 percent and the fault detection latency is less than 80 ms, and TFT achieves the lowest errors by surpassing traditional approaches and deep learning methods with a proportional forecast error of 2.36 and 2.91 precent on day-ahead and week-ahead horizons respectively. These techniques, when integrated into an AI-enabled smart grid system appear to be the best way to improve operational reliability, efficiency, and predictive performance of the smart grid in comparison to legacy techniques. The results demonstrate that AI has the power to reshape the smart grid operations entirely and shift it to the proactive actions of optimization rather than reactive conditions management, yet there are important questions associated with applying it to real-life that should be taken into account and discussed.

Keywords: 

Artificial Intelligence, Smart Grids, DeepReinforcement Learning,CNN–LSTM, TemporalFusionTransformer, Volt/VAR Optimization, FaultDetection,LoadForecasting, Distributed Energy Resources, Grid Reliability