Smart Grid Technology and AI Integration for Renewable Energy Management

Author

Ahire Sachin Rambhau,Shruti Sunil Bunge, Dhanshri Shantaram Dhanwate

Abstract

 The rapid growth of solar and wind power is making electricity networks harder to operate, because renewable output is variable, weather dependent and difficult to predict. Smart grid technology, which adds sensing, two-way communication and automation to the power system, together with artificial intelligence (AI), offers a practical way to manage this variability. This paper reviews the main smart grid technologies and AI techniques used for renewable energy management, and proposes a layered AI-integrated energy management system (EMS) for a solar–battery microgrid. The proposed EMS forecasts solar generation one day ahead and uses the forecast to schedule battery charging and discharging against a time-of-use tariff. A reproducible simulation study with synthetic data (50 kWp solar array, 100 kWh battery, 30 test days) shows that a battery with simple rule-based control reduces electricity cost by 20.2 %, forecast-driven scheduling with a random forest model raises the saving to 22.2 %, and a perfect forecast would give 24.7 %. Challenges such as cyber-security, data quality and cost are discussed, and the paper is written to be accessible to diploma-level engineering students.

Keywords

Smart grid, artificial intelligence, renewable energy, energy management system, solar forecasting, battery storage, microgrid, machine learning.

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References

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