Artificial Intelligence-Enabled Optimization of Power Grids with High Penetration of Renewable Energy

Integrating high shares of renewable energy (۳۵–۶۵%) into power grids presents significant challenges for stability and efficiency, demanding solutions beyond conventional management approaches. This study explores AI-driven optimization for enhancing grid performance using hybrid neural network-genetic algorithm models and deep reinforcement learning (DRL) controllers. High-resolution datasets from ENTSO-E and NREL, along with IEEE benchmark-validated simulations, evaluate AI's impact on forecasting accuracy, frequency regulation, and operational costs under varying renewable integration scenarios.Key findings demonstrate a ۲۳.۴% improvement in frequency stability (p < ۰.۰۰۱), a ۱۹.۱% reduction in operational costs, and ۹۹.۲% reliability during extreme weather events. The hybrid model achieved a ۳۸% enhancement in forecasting accuracy (۴.۲% error) with sub-۱۵ms latency, while the DRL controller prevented ۹۴.۳% of outages with a ۲.۷-second response time. Economic analysis revealed a ۳–۵ year payback period and annual savings of $۱.۸ million per ۱۰% increase in renewable penetration.These results underscore AI's superiority over traditional methods in mitigating renewable intermittency but highlight persistent challenges in real-world deployment, such as infrastructure heterogeneity and high upfront costs. The study concludes that AI-based optimization can significantly advance sustainable energy systems, though further research is needed on edge computing, interoperability standards, and socio-technical integration for scalable implementation. This work provides critical insights for policymakers and grid operators managing the transition to high-renewable energy systems.

کلیدواژه ها:

power system optimization ، renewable energy integration ، artificial intelligence ، deep reinforcement learning ، predictive control