Examination of pricing and generation-related load balance in smart grids

Today, as a result of the increasing need for electrical energy and the decrease in fossil fuels in the world, the use of renewable energy sources in electricity generation has become widespread. The integration of renewable energy sources into power systems and the diversity of distributed energy sources, as well as the requirements for power systems to be efficient, reliable and manageable, are realized with smart grid technology. In smart grids; In addition to customers' changing demands and the desire to use electrical energy at the most affordable price, generation systems' adjustment of generation under optimum conditions and suppliers' profits require electricity market load balance management with a common coordination. In order to ensure an effective load balance, it is necessary to benefit from a dynamic pricing strategy. In this thesis, the load balance based on dynamic pricing is controlled by using a Closed Loop Electrical Power Control System (KÇEGKS) Modeling, where the demand-supply balance and the electric energy price meet at a common point. In the system model, three different controllers were used in accordance with the smart grid technology structure to achieve the demand-supply-price balance in the electricity market. Classical PI controller, Fuzzy Logic PI controller and Neural Network Model Reference controller structures have been applied to different demand change scenarios within the scope of the thesis study. For each demand change scenario, controllers were used separately and performance comparisons were made according to the selected criteria. The obtained simulation results were shown on the same graphs and the advantages and disadvantages of the controllers were evaluated.