Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy

This research investigates the scheduling of tasks in real-time to optimize energy use in the context of integrating renewable energy sources into the smart grid. The primary goals are to analyze the influence of fluctuations in renewable energy on grid synchronization, evaluate the efficiency of different optimization methods, and identify significant obstacles and corresponding remedies. Secondary data studies advanced forecasting methods, energy storage systems, and optimization techniques, including Linear Programming (LP), Dynamic Programming (DP), and metaheuristics. The significant findings show that renewable energy fluctuations affect power system stability. Advanced prediction methods and energy storage are essential in reducing these impacts. Optimization approaches enhance the scheduling efficiency, but the computational complexity and practical application constraints limit their effectiveness. Challenges such as frequency regulation, voltage management, and integrating Distributed Energy Resources (DERs) need specific solutions such as dynamic voltage support and grid modernization. The policy implications include supporting advanced technologies, encouraging real-time scheduling system research, and enhancing grid infrastructure to increase resilience. These measures are essential for integrating renewable energy, ensuring a reliable smart grid, and achieving a sustainable future.