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PE(I), CE(I), Freelance Consultant, Add:46, Mother Teresa Lane, Bhetapara Road, Hatigaon, Guwahati-781038, INDIA
Abstract: (425 Views)
The complexities of operation and expansion planning of a power grid in recent time have grown in fast pace primarily due to integration of variable Renewable Energies (RE) and variable load demand. To achieve precise and reliable grid management, two critical functions—prediction and optimization must be executed with high accuracy. Conventional analytical frameworks, however, increasingly fail to deliver robust solutions for these tasks, particularly when processing vast, high-dimensional datasets in real time. This gap underscores the necessity for advanced computational algorithms capable of extracting actionable insights from complex data streams. Artificial Intelligence (AI), a domain focused on developing systems that mimic human cognitive processes, has emerged as a transformative tool for addressing these challenges. Its applications span critical areas such as load forecasting, RE generation prediction, and Economic Load Dispatch (ELD) optimization, where it outperforms traditional statistical methods in scalability and precision. This paper reviews widely adopted AI techniques tailored to core grid applications and industry-standard software solutions that leverage these technologies. By synthesizing advancements in AI-driven tools, the study highlights their potential to redefine grid resilience, efficiency, and operational flexibility in an era of energy transition.
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