Construction of Smart Regulation and Monitoring System for High-Penetration Renewable Energy

With the increasing penetration of renewable energy, its intermittency and volatility pose significant challenges to power grid stability and optimal scheduling. Traditional scheduling methods struggle to accommodate uncertainties, thereby affecting the security and economic efficiency of the system. To address these challenges, this paper proposes an intelligent regulation and monitoring system for high-penetration renewable energy. The system architecture integrates data acquisition, intelligent analysis, optimized scheduling, and visualization, establishing a closed-loop process from data collection to intelligent decision-making. Leveraging artificial intelligence and big data technologies, a high-precision load forecasting model and an optimized scheduling strategy are developed to enhance renewable energy integration and control accuracy. Additionally, a real-time visualization monitoring platform is implemented to facilitate intelligent monitoring and predictive analysis of multiple energy carriers. This system is applicable not only to energy management scenarios but also provides data support for power flow analysis, grid dispatch simulations, and carbon intensity calculations. The findings of this study offer theoretical foundations and technical insights for the development of smart grids, microgrids, and integrated energy management systems, demonstrating significant academic value and practical applicability.