Research has shown how Machine Learning better predicts solar PV output, and how AI models improve integrated power stations. AI, particularly through fuzzy logic control (FLC), also significantly enhances the efficiency and reliability of hybrid energy storage systems (HESS) used in conjunction with renewable energy sources (RES). Here are some key ways AI improves renewable energy systems:
- Enhanced Energy Management:
- AI-based fuzzy logic control helps manage the charging and discharging cycles of energy storage systems, balancing power distribution between batteries and supercapacitors. This ensures that power demands are met efficiently while extending the lifespan of the storage systems.
- Optimised Power Distribution:
- FLC allows for real-time adjustments in power distribution, accounting for variables such as the state of charge (SOC) of both batteries and supercapacitors, and the power produced by photovoltaic systems. This dynamic response improves the overall stability and performance of the energy systems.
- Prolonged Battery Life:
- By intelligently controlling the workload on batteries and integrating supercapacitors for short-term high power demands, FLC reduces the stress on batteries, thus prolonging their operational life and improving system reliability.
- Handling Intermittency of Renewable Sources:
- The intermittent nature of RES, such as solar and wind power, poses challenges for consistent energy supply. AI-driven HESS can mitigate these fluctuations, ensuring a steady supply of electricity even when the renewable source is not generating power at its peak.
- Simulation and Performance Analysis:
- AI models, implemented through platforms like MATLAB/SIMULINK, allow for thorough testing and simulation of different scenarios. This helps in refining the control strategies and predicting the performance of the HESS under various conditions, leading to more robust and reliable energy systems.
By leveraging AI, particularly through fuzzy logic control, the integration of HESS with renewable energy systems becomes more efficient, reliable, and sustainable, addressing the challenges of intermittency and energy management in RES.
Source
Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023), Atlantis Press, 2024-06-29
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