This study will examine how to integrate renewable energy systems, which mainly focus on solar and wind power sources, into the contemporary power systems based on mechatronic concepts and artificial intelligence (AI). As the global energy systems become decarbonized, there has been the need to ensure that issues like grid instability, support for intermittent energy production, and efficient load management are addressed using technology. The goal of the study consists of the modeling of smart grid operations through the coupling of simulation with neuro-network-based forecasting and optimization methods that include neural networks and real-time control algorithms. The research suggests a critical evaluation of mechatronic components, sensors, actuators, and converters in providing responsive and adaptive grid architecture based on secondary data using a qualitative research approach. AI is used to improve the performance of load prediction and demand-side management and mitigate energy wastage. Cybersecurity, financial, and governance implications are evaluated, and scalability and fault detection are considered key performance indicators. The projected benefits will be enhanced grid efficiency, enhanced reliability with real-time decision-making, and resilience to operational upheavals. The results will hopefully feed into policies and technology standards that allow energy transitions that are secure, scalable, and financially tenable. This paper explains the importance of interdisciplinary efforts in linking AI-powered energy systems with the overall dreams of sustainability and equity.
A Fri, study studied this question.