AI-Driven Integrated Optimization for Virtual Power Plants in Smart Grids

This paper delves into the potential of AI-driven optimization algorithm for integrating virtual power plant (VPP) into the electrical grid, aiming to enhance grid efficiency and maximize the utilization of renewable energy sources. By examining the fundamental principles of these advanced algorithms and their crucial role in enhancing power systems, this study demonstrates how artificial intelligence (AI) can improve both the accuracy and efficiency of optimization processes. Through a comprehensive comparative analysis of existing optimization techniques, our research highlights their inherent strengths and limitations, thereby providing a solid foundation for informed and innovative algorithm design. We propose a sophisticated AI-centric optimization framework that underscores the critical importance of robust data acquisition and pro-cessing mechanisms. The implementation, testing, and validation of our algorithm demonstrate its efficacy, indicating sig-nificant potential to enhance VPP operations. This work reinforces the theoretical foundation necessary for advancing the intelligence of VPP and offers valuable technical insights. It serves as a guiding beacon in promoting sustainable energy uti-lization while deepening our understanding of AI's profound impact on power system integration and optimization. Fur-thermore, our research provides novel perspectives and practical recommendations for future advancements in Smart Grid, contributing meaningfully to this burgeoning field.