Real-time dynamic energy pricing methods in smart grids

The energy demand is gradually increasing due to the change in the daily essential needs and efforts of the users to adapt to technological developments and this situation affects the existing grid structures. Increasing energy demand causes either the establishment of new production facilities or an increase in foreign dependency on energy. These situations may adversely affect the economy and may also affect the amount of greenhouse gases that cause global warming to increase with the use of fossil fuels in new power plants. For these reasons, the concept of smart grid gain has emerged. Renewable energy sources, which are easier to integrate into the smart grid, are an alternative to the current situation. In order to provide energy efficiency against fluctuating production of renewable energy resources and variable fluctuations in demand, the dynamic processes of energy prices lead to important developments under the title of demand side load management. In this study, closed-loop control techniques were utilized to control the uncertain production of renewable energy sources and user consumption. In this way, a real-time energy pricing approach was proposed. For this purpose, a first-order system was established, and then the controller parameters were calculated using the Ziegler-Nichols frequency response and the pole placement methods. These calculated controller parameters were compared with the controller parameters, which were obtained with Particle Swarm Optimization (PSO), and the appropriate ones were selected parameters. Energy price signals were obtained by using these selected parameters for the first-order system. It has been concluded that real-time dynamic energy pricing is effective for both flexible demand control and immediate response to fluctuating production. The simulation results demonstrate that the dynamic energy price processes using the controller provide efficiency by constructing an energy balance. Keywords: Controller-based dynamic energy pricing, energy balance, renewable energy integration, particle swarm optimization