AI-Powered Smart Grid for Sustainable Energy Distribution: A Comprehensive Simulation and Optimization Framework

The paper presents a novel AI-based simulation model that can be applied in the optimization of sustainable energy distribution in Smart Grids (SG). The framework integrates the energy demand forecasting, model of renewable energy generation, and online grid optimization relying on the features of the recent machine learning algorithms, including Random Forest Regressor. The model is trained on a synthetical dataset, which contains seasonal variability of demand, and renewable sources of power which comprise of solar and wind power. The performance of the demand forecasting model has also been evaluated in key measures with the coefficient of determination (R2) of 0.80 and mean squared error (MSE) of 7.25 as good forecasting performance of the model. Otherwise, a Watts-Strogatz graph is a simulated software that is designed to represent a smart grid network, with consumers, producers and storage units being the nodes. The allocation of resources is also efficient and the energy allocation within the network is optimized based on the nature of the nodes and their carrying capabilities. Real-time simulations suggest that the system maintains a balance between supply and demand of 100 percent, and renewable energy share of 46.5. The results emphasize the AI possibilities in enhancing Smart Grids performance with the integration of real-time monitoring, optimization, and detection of anomalies. This framework forms a platform onto which the Smart grid applications of the future can be designed with the focus on sustainability and efficiency of operation.