FREMONT, CA: Artificial intelligence (AI) and machine learning (ML) have emerged as critical technical solutions as the industry seeks to meet the fast-growing need for clean, affordable, and dependable energy. These cutting-edge technologies can analyze the past, optimize the present, and forecast the future. This means that AI and ML can address the majority of the present difficulties.
With rapid improvements in technology, the renewable energy sector has made great strides over the last decade. However, a few remaining difficulties can be handled with the help of AI and ML.
When it comes to renewable energy, the electric grid is one of the most complex machinery. Grids now have numerous issues in handling the diversity of renewable energy sources due to wind and solar energy's inherent instability. This necessitates the development of intelligent systems capable of expanding renewable energy's integration into the existing grid and establishing renewable energy as an equal partner in the energy supply. This section discusses how artificial intelligence technology enhances the reliability of renewable energy and modernizes the overall system.
Control Centers That Are Intelligent and Centralized
A grid's devices and sensors are typically connected to capture massive volumes of data. When AI is included in the system, this data can provide grid operators with fresh insights. It provides energy suppliers with the ability to match supply and demand intelligently. Advanced load management systems can be implemented in conjunction with industrial furnaces or colossal air conditioning units that can shut down automatically when the power supply is low. Additionally, intelligent storage units can be changed in response to supply flow. Additionally, by forecasting weather and load using smart and advanced sensors, people can increase the total integration and efficiency of renewable energy.
Improved Microgrid Integration
Microgrids have gained widespread adoption as a means of managing dispersed energy. Artificial intelligence-assisted control optimization can help resolve power quality concerns and bottlenecks. Intelligent algorithms may be able to provide real-time control over the dynamic nature of the grid beneath the autonomous substation controls. Algorithms made available by AI can also aid in system optimization as new devices and energy sources are integrated.
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