AI-Augmented Microgrid Design for Renewable Energy Distribution Networks
DOI:
https://doi.org/10.64137/XXXXXXXX/IJETET-V1I1P105Keywords:
AI-driven microgrids, Renewable energy integration, Predictive maintenance, Load forecasting, Energy storage optimization, Machine learning algorithms, Resilience enhancementAbstract
Renewable energy distribution systems are being improved thanks to AI in microgrids, which solve the issues caused by fluctuations in supply and demand. Intelligent systems that use machine learning and predictive analytics are used to help with the generation, storage and delivery of energy. Using recent information from IoT sensors and earlier energy usage records, LSTM and SVR AI algorithms are able to predict load accurately, balance any changes in load smoothly and allow proactive energy management. As a result, there is effective energy use from solar, wind and hybrid renewables with barely any need for backup generators. Innovations such as using AI for predictive maintenance reduce downtime of equipment by about 30–40% and Genetic Algorithms (GA) can lead to up to a 67% drop in electricity costs in comparison to more traditional approaches. Using adaptive control allows these microgrids to manage the gap between supply and demand, make the grid more stable and prevent problems caused by changing weather by shifting resources early. Rural case studies prove that it is possible to enhance both energy supply reliability and decrease carbon emissions by around 50%. Using AI makes energy networks safer and lets them use demand response to motivate people to use energy when demand is low. With these advancements, AI-enhanced microgrids become key support for reliable, cheaper and greener energy in both areas with and without a main energy network
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