Renewable Energy Optimization in the Electric Grid: A Systematic Review of Models and Technologies

Abstract

The integration of renewable energy sources into the electric grid presents a promising solution for sustainable energy production. However, optimizing the utilization of renewable energy within the grid poses significant challenges due to the inherent variability of these sources. This systematic review explores various models and technologies for optimizing renewable energy in the electric grid. Through comprehensive literature review and data analysis, diverse optimization approaches including mathematical optimization, machine learning, neural networks, and genetic algorithms were identified. Comparative analysis revealed the strengths, weaknesses, and applicability of these approaches in different contexts. Case studies highlighted the effectiveness of optimization strategies in improving grid performance. Discussions encompassed challenges such as model complexity, data accuracy, and regulatory constraints, while also identifying opportunities for technological innovation and policy support. This review contributes to a deeper understanding of renewable energy optimization and informs future research directions for enhancing grid sustainability.