Data-driven closed-loop decision support system for renewable energy management in smart grids with integrated forecasting, optimization, and storage modeling

This paper examines the integration of renewable energy management within smart grids through a decision support system (DSS) that leverages data analysis techniques. As wind and solar energy sources continue to expand, managing their inherent intermittency becomes a critical challenge. To address this, the paper develops a mathematical model designed to optimize energy production, consumption, and storage using simulated data validated against published industry benchmarks and operational patterns. The primary objective of the model is to minimize energy waste and ensure a reliable, cost-effective energy supply while maximizing the utilization of renewable resources. An empirical study is conducted using benchmark-aligned simulated smart grid data, demonstrating the effectiveness of the DSS in optimizing grid management. The study finds that the integration of the DSS leads to a 12.5% reduction in operational costs compared to a baseline scenario (with confidence intervals calculated through Monte Carlo simulations to account for variability in renewable generation), with renewable energy contributing to 18% more of the total energy supplied to the grid. Battery storage efficiency, degradation, and charging/discharging constraints are incorporated into the model, enhancing grid stability. These findings highlight the importance of advanced forecasting and optimization techniques in smart grid operations, while acknowledging the limitations of simulated data and emphasizing the need for future validation using real operational datasets.