Useful Application of Machine learning Methods in Smart Grids: A Mini Review

Smart grids are necessary because the traditional electricity grids are outdated, inefficient, and vulnerable to failures. Smart grids enable better monitoring, control, and management of the electricity grid, ensuring a more reliable and stable power supply. They can integrate renewable energy sources into the grid, reduce energy consumption during peak hours, and improve resilience to natural disasters and cyber threats. On the other hand, machine learning techniques are necessary in modern power systems to improve the performance, efficiency, and sustainability of the electricity grid. They enable real-time monitoring and control of the grid, predicting energy consumption patterns, optimizing grid performance, and detecting anomalies. By integrating machine learning algorithms, power systems can adjust their outputs to match changes in energy demand, improve renewable penetration and reduce carbon emissions. They also provide insights that can guide decision-making, improve asset management, and reduce maintenance costs. With the integration of machine learning techniques, power systems can promote a more sustainable and reliable power supply, enhances grid security, and improves the experience for both energy providers and end-users. Thus, this paper aims to summarize the benefits and useful application of machine learning methods within the smart grids.