A fuzzy logic reasoning based real time energy price regulation approach for smart grids

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