Intelligent technologies have become pioneer force to provide flexible, dynamic and efficient energy generation and management. Thus, smart algorithms such as fuzzy logic, artificial neural network, machine learning, soft computing techniques are sole remedy against growing diverse and numerous distributed generations that make more complicated power system. Real time closed loop controlling requires energy price as a featured variable to procure supply demand equilibrium point for stable and reliable power system operation, where several dynamic models and estimation software are introduced in the literature. In this study, a fuzzy logic reasoning based price regulator (FLR-PR) is designed and simulated on MATLAB/Simulink environment using 2018 hourly data of a summer day taken from annual energy report of Turkey. Proposed model has been compared based on performance indexes to Proportional Integral Derivative (PID) price controller in the constituted simulation cases. FLR-PR tracks instant reference demand signal changes with minimum steady state error and fast transient response with respect to PID controller.