Renewable energy sources (RESs) enable sustainable and environmentally friendly electricity generation. However, their intermittent nature makes it difficult to maintain energy balance. Smart grids (SGs) address this challenge by enabling grid control under variable demand and fluctuating generation. With the growing share of distributed generation, dynamic energy pricing has become increasingly important for sustaining the supply–demand balance in SGs. This study aims to regulate the interaction between variable demand and distributed generation in SGs using control strategies. The dynamic pricing framework was analyzed using closed-loop Proportional–Integral–Derivative (PID), Fractional-Order PID (FOPID)- and Adaptive Neuro Fuzzy Inference System (ANFIS)-based FOPID controllers. PID and FOPID parameters were tuned by pole placement with reference model matching, while the FOPID parameters in the ANFIS-FOPID structure were adaptively optimized using ANFIS. Energy supply–demand models were developed in MATLAB/Simulink, and the effects of each controller on system dynamics and energy prices were comparatively examined. The results indicate that ANFIS-FOPID achieves lower overshoot, shorter settling time, and more stable balancing performance, owing to its fractional-order flexibility and optimized parameters. In the model established in the MATLAB/Simulink environment, the controllers were evaluated based on integral of squared error (ISE), time-weighted integral absolute error (ITAE), root mean square error (RMSE), average unit energy price, price volatility, and coefficient of variation. A virtual energy storage model was added to the system. Disturbance and load change scenarios were also examined. The results showed that the ANFIS-FOPID controller provided the most balanced performance in terms of error reduction, suppression of price fluctuations, and reduction of the average unit energy price.
YALÇINÖZ et al. (Tue,) studied this question.