Adaptive Hybrid Energy Systems for Reliable Renewable Integration

The highest growth ever of the renewable energy sources has raised issues with respect to their reliance on the uncertainties of solar and wind power. The combination of the renewable energy sources with backup and storage plants in Hybrid Energy Systems (HES) has appeared to be promising; however, HES configurations are rarely adaptable to varying operating conditions. This dissertation formulates and contrasts Adaptive Hybrid Energy Systems (AHES) frameworks, HES with solar, wind, and battery backup and grid backup systems managed by an intelligent adaptive control system. The system applies AHES forecasting and optimization techniques to enhance system reliability through dynamically adapting the supply of energy to limit ‘Power Supply Loss Probability, a statistical expectation of a system to fall below a specified prescribed Power Supply Loss threshold level.’. The proof of concept is realized in MATLAB and Simulink where models with varying energy requirements and aggregation of meteorological systems are simulated. The adaptive strategy enhanced the reliability of renewable energy supply, and the cost of energy was lowered by 15% and the conventional share of renewables deployed by integrated systems.