Optimizing photovoltaic integration in grid management via a deep learning-based scenario analysis

Abstract

Abstract Addressing the challenges of integrating photovoltaic (PV) systems into power grids, this research develops a dual-phase optimization model incorporating deep learning techniques. Given the fluctuating nature of solar energy, the study employs Generative Adversarial Networks (GANs) to simulate diverse and high-resolution energy generation-consumption patterns. These synthetic scenarios are subsequently utilized within a real-time adaptive control framework, allowing for dynamic adjustments in operational strategies that enhance both efficiency and grid stability. By leveraging this approach, the model has demonstrated substantial improvements in economic and environmental performance, achieving up to 96% efficiency while reducing energy expenses by 20%, lowering carbon emissions by 30%, and cutting annual operational downtime by half (from 120 to 60 h). Through a scenario-driven predictive analysis, this framework provides data-driven optimization for energy systems, strengthening their resilience against renewable energy intermittency. Furthermore, the integration of AI-enhanced forecasting techniques ensures proactive decision-making, supporting a sustainable transition toward greener energy solutions.

Keywords

• AI in energy systems
• Energy forecasting
• Grid optimization
• Machine learning
• Renewable integration
• Scenario generation