A Dynamic Feed-in Tariff Model for Photovoltaic Systems Utilizing Adaptive Fuzzy Systems: An Australian Perspective

This study focuses on developing and implementing a dynamic feed-in tariff (FiT) model tailored for residential solar photovoltaic (PV) systems in Australia. The proposed approach combines adaptive fuzzy logic with Monte Carlo simulations to effectively manage the uncertainties associated with renewable energy generation. The framework segments the day into five distinct zones, following Australian energy provider policies, and dynamically adjusts fuzzy rules to suit the specific conditions of each zone. Initially, FiT values are calculated separately for each input parameter. Subsequently, Shapley Additive Explanations (SHAP) is employed to evaluate the individual FiT that contributes to the overall ensemble FiT. According to the numerical analysis, the suggested model can easily adapt to real-time energy consumption and generation changes. This adaptive methodology ensures the FiT is responsive to real-time energy conditions while adhering to government policies for integrating renewable energy.