OPTIMIZING RENEWABLE ENERGY INTEGRATION AND DEMAND RESPONSE THROUGH AI-DRIVEN CONTROL ALGORITHMS IN SMART GRIDS

Teena Vats; Ritesh Patel; Mukesh Kumar; Anil Kumar

doi:10.29121/shodhkosh.v5.i1.2024.5509

Authors

Teena Vats Department of Electrical Engineering, Sabarmati University, Ahmedabad, Gujarat (India)
Ritesh Patel Department of Electrical Engineering, Sabarmati University, Ahmedabad, Gujarat (India)
Mukesh Kumar Department of Physics, Swami Shraddhanand College, University of Delhi (India)
Anil Kumar Department of Applied Science, Bharati Vidyapeeth’s College of Engineering, New Delhi (India)

DOI:

https://doi.org/10.29121/shodhkosh.v5.i1.2024.5509

Keywords:

Artificial Intelligence, Smart Grids, Renewable Energy Integration, Demand Response, Control Algorithms, Machine Learning, Energy Forecasting, Grid Stability, Sustainable Energy, Real-Time Load Management

Abstract [English]

The global shift toward sustainable energy solutions has intensified the integration of renewable energy sources into power grids. However, the intermittent and unpredictable nature of renewables poses challenges in maintaining grid stability and reliability. This paper explores the role of artificial intelligence (AI)-driven control algorithms in optimizing renewable energy integration and enhancing demand response strategies in smart grids. Through a descriptive analysis, it highlights how machine learning, neural networks, and predictive analytics enable real-time energy forecasting, adaptive load management, and automated grid control. The study also examines the potential of AI to balance energy supply and demand while reducing reliance on fossil fuels. Furthermore, the research underscores the synergy between AI technologies and smart grid infrastructure in paving the way for a more resilient, efficient, and environmentally sustainable power system.

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