Advancing wind energy systems with predictive control and efficiency optimization in smart grids

Relevance. Integrating wind power presents unique challenges due to its intermittent nature, but smart grids offer a solution. Smart grids, with their advanced communication, control, and automation capabilities, provide an ideal platform for managing the variability of wind power. By utilizing real-time data and intelligent algorithms, smart grids can dynamically balance supply and demand, maximizing wind power and other renewable sources. This synergy significantly enhances the stability and security of the power grid while also improving its overall efficiency. Aim. To combine predictive control model and AI-based techniques with adaptive control, as well as real-time monitoring to show excellent performance in terms of system stability, energy efficiency, improved economic viability. Objects. Wind energy systems and how smart grids can be utilized to do better control of it. Methods. Comprehensive methodology to enhance wind energy systems by employing predictive control and improving efficiency in smart grids. It integrates advanced, model-based optimization methods (e.g., predictive control model) and learning control schemes using artificial intelligence approaches within efficiency-oriented on-line evolutionary strategies that support real-time monitoring and adaptive modeling. This methodology encompasses three primary phases: system modeling, predictive control application, and efficiency maximization. Results. These research findings point out the significant rooms for improvement concerning the predictive control and efficiency optimization strategy on the smart grid-integrated wind systems. The study illustrates that integrating them with advanced control strategies such as predictive control model and AI techniques may provide a solution to bring significant improvements in system stability and energy efficiency. By utilizing predictive algorithms, these novel methods can predict the variations of future wind generation and act proactively to reduce fluctuations in wind power generation providing a more stable output with minimum losses (greater reliability).